Executive Summary

This article explores the neuroscience that makes BCIs possible, examining how different brain wave patterns encode our intentions, why certain brain regions provide optimal signals for BCI control, and how the remarkable plasticity of our neural networks enables a unique partnership between mind and machine.

Table Of Contents

1. Introduction
2. Historical Context
3. What Are Brain-Computer Interfaces (BCIs)?
4. What Is The Brain?
5. What Are Brain Waves?
6. What Are The Types Of Brain Waves?
7. How Do Neurons Generate BCI Detectable Electrical Signals?
8. What Are Spontaneous & Evoked Potential Electrical Signals?
9. How Quickly Do Signals Travel?
10. Why Do Brain Signals Get Weaker As They Travel?
11. Why Are Brain Waves & Electrical Signals The BCI Focus?
12. Which Parts Of The Brain Are Best For Electrical Signal Acquisition By BCIs?
13. How Does The Brain’s Plasticity Affect BCI Signal Capture & Interpretation?
14. Final Thoughts
15. Appendix

1. Introduction

BCIs are a technology at a fascinating inflection point – we understand neuroscience well enough to capture and interpret brain signals, yet the technology for processing these signals remains a bottleneck. As signal processing algorithms become more sophisticated, and our understanding of neural encoding deepens, we’re witnessing the transformation of BCIs from laboratory demonstrations to practical tools for communication, control, and rehabilitation [11, 18, 21]. The story of brain-computer interfaces is ultimately one of convergence – where our growing understanding of the brain’s electrical language meets advancing technology capable of translating that language into action. It’s a testament to human ingenuity that we’ve learned to eavesdrop on the brain’s electrical conversations and translate them into meaningful control, opening new channels of communication for those who need them most [11, 21].

2. Historical Context

The field of brain-computer interfaces traces its origins to 1875 when Dr. Richard Caton first observed electrical currents in animal brains, but gained momentum with Hans Berger’s groundbreaking EEG recordings in the 1920s and Jacques Vidal’s formal articulation of the BCI concept in 1973. Early milestones included the development of the P300 speller system in 1988, which enabled users to type by focusing on flashing letters, and the first human BCI implant in 1998. The 2000s witnessed rapid technological advancement, from wheelchair control via EEG in 2005 to tetraplegic patients controlling robotic limbs through cortical implants by 2012. Recent years have seen accelerating commercial development, with companies like Synchron performing the first US Stentrode implant in 2022, Neuralink receiving FDA approval for human trials in 2023 and achieving its first human implant in 2024, and Paradromics completing its inaugural human BCI procedure in 2025, signaling the transition of BCIs from experimental technology to clinical reality. (See the Chronology in the Appendix for more historical context)

3. What Are Brain-Computer Interfaces (BCIs)?

As defined at the first International BCI Conference, a brain-computer interface is “a communication system that does not depend on the normal output path composed of peripheral nerves and muscles [11, 23, 24].” More precisely, BCIs are devices that directly interface with the brain to enable high-fidelity recording and modulation of neurons, establishing a “lingua franca” among neural and digital signals and creating a channel for passing brain data from the biological to the technological realm (and vice versa) [9, 11, 12, 13, 19, 25, 115, 116]. Unlike brain imaging technologies that provide only diagnostic information, BCIs enable real-time control and interaction [21]. The brain computer interface is a direct, and sometimes bidirectional, communication tie-up between the brain and a computer (both hardware and software) and/or an external device [3, 4, 5, 7, 8, 9, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 116]. BCIs convert brain signals into computer-driven outputs [9, 16, 26, 116], enabling humans to interact with their surroundings without the involvement of peripheral nerves and muscles [5, 9, 11, 14, 17, 18]. This distinguishes them from other neural technologies that may require muscle involvement [5, 6, 7].

4. What Is The Brain?

The brain, a complex organ that controls thought, memory, emotion, touch, motor skills, vision, breathing, temperature, hunger and every process that regulates our bodies [140, 141, 147], is fundamentally an information processing system – a “data organ” that receives information about the world through the senses, processes that information, and then sends control signals to the muscles to respond to the environment [115, 148]. This understanding forms the foundation of neuroengineering and the development of brain-computer interfaces (BCIs) as human interface devices that can seamlessly connect with the brain [1, 2, 3, 4, 5, 6, 7, 8, 12, 13, 14, 15, 17, 18, 19, 20, 21, 23, 25, 26, 27, 114, 115]. Structurally, weighing near 3 pounds in the average adult [140, 141], the brain is about 60% fat and 40% a combination of water, protein, carbohydrates and salts [140]. At a high level, the brain can be divided into three parts – the cerebrum, brainstem and cerebellum [140, 141]. The largest part of the brain, making up approximately 80% of the brain’s total volume [147], the cerebrum (front of brain) comprises gray matter (the cerebral cortex) [149, 150] with white matter at its center [140]; the cerebral cortex is formed of billions of neurons [151], the primary functional unit and fundamental building block of the human brain [141, 147], and the importance of white matter fiber pathways in shaping functional brain networks is well-known [142, 143, 144, 145, 146].

5. What Are Brain Waves?

Brain waves are rhythmic, oscillatory patterns of electrical neural activity that occur at different frequencies, each of which is associated with certain mental states and cognitive processes [5, 7, 9, 12, 16, 17, 19, 20, 24]. These rhythms, particularly the sensorimotor rhythms that include mu (8-13 Hz) and beta components over the motor cortex, are fundamental to many BCI control paradigms; users can learn to voluntarily modulate these rhythms through motor imagery or other mental strategies, providing reliable control signals for BCI applications [5, 20]. In addition, in BCI applications, the different brain wave frequency bands provide valuable information about the user’s mental state and intentions [5, 6, 7, 9]. For example, in motor imagery BCIs, which primarily utilize alpha and beta bands due to their clear modulation during movement planning and execution [6], changes in μ rhythms (8-13 Hz) and β rhythms (13-30 Hz) over the motor cortex are particularly important for detecting imagined movements. Abnormalities in specific frequency bands can also indicate pathological conditions; for instance, abnormal theta and alpha wave patterns during cognitive tasks can indicate the presence of neurological disorders like Alzheimer’s disease [7].

6. What Are The Types Of Brain Waves?

Brain wave types are typically discussed in order of their oscillatory patterns, from lowest Hz to highest Hz. Note, though, that brain wave frequency boundaries vary slightly across research literature due to different measurement techniques, cultural traditions in neuroscience labs, and specific applications. This document uses standardized ranges of: delta: 0.5-4 Hz, theta: 4-8 Hz, alpha: 8-13 Hz, beta: 13-30 Hz, gamma: 30-100 Hz, but acknowledges that some researchers (such as those referenced at 1, 5, 6, 7, 12, 16, 17, 19, 20, 23) may use slightly different boundaries.

Delta Waves

Delta waves, oscillating between 0.5 and 4 Hz and the slowest brain waves, are primarily associated with deep sleep states, dreamless sleep, and unconscious states [5, 6, 12, 16, 17]. While common in babies, they decrease with age and are unusual in awake adults [5, 6, 12]. A large amount of delta activity in an awake adult often indicates neurological disease [5].

Theta Waves

Theta waves, ranging from 4 to 8 Hz, appear in drowsy or meditative states (and the transition between waking and sleeping states) [5, 6, 7, 12, 17]. Young children show more theta activity, while in adults these waves play important roles in memory formation and spatial navigation and are associated with meditative concentration and various cognitive processes including mental calculation, maze navigation, and conscious awareness. Only small amounts of theta frequencies are normally recorded in awake adults [5, 6].

Alpha Waves

Alpha rhythms, found in the 8 to 13 Hz range, originate primarily in the occipital region and are most prominent when the eyes are closed and the body is relaxed, during idle states when the mind is at rest [5, 6, 7, 16, 17, 20]. More specifically, alpha waves divide into low alpha at 8-10 Hz, associated with wakeful relaxation and calmness without attention or concentration, and high alpha at 10-12 Hz, linked to increased self-awareness, focus, and learning of new information [12]. These rhythms are strongest over the occipital regions and attenuate when the eyes open or when mental effort is exerted. They primarily reflect visual processing, but may also be related to memory functions. Increasing mental effort causes suppression of alpha activity, particularly in frontal areas, making these rhythms useful for measuring cognitive load [5, 6, 7, 20]. Alpha waves show characteristic suppression during motor tasks, proving critical for distinguishing individual finger movements and serving as an important baseline in BCI applications [6, 19].

Beta Waves

Beta rhythms, within the 13 to 30 Hz range, are recorded in the frontal and central regions and are closely associated with motor activities and active thinking and focus [5, 6, 12, 16, 17, 20]. More specifically, beta waves split into low beta at 13-18 Hz during active thinking, attention, and problem-solving, and high beta at 18-30 Hz during intense mental activity and alertness [12]. These rhythms desynchronize during actual movement or motor imagery (physical movement preparation) and show symmetrical distribution when there is no motor activity – providing valuable signals for motor BCIs [5, 6, 16, 19, 20].

Gamma Waves

Gamma rhythms, ranging from 30 to 100 Hz, relate to various motor functions and perceptions in healthy adults [1, 5, 6, 7, 12, 16, 17, 20]. More specifically, gamma waves are separated into two ranges: a low gamma range of from 30-50 Hz, and a high gamma range of from 50-100 Hz [12]. Gamma waves are linked to high-level cognitive processing, attention, and consciousness, and appear to play a role in binding aspects of perception into unified experiences [5, 6, 7, 17, 20]. Gamma rhythms are less commonly used in EEG-based BCI systems because artifacts from muscle activity and eye movements are likely to negatively affect them, though they may offer higher information transfer rates and better spatial specificity than traditional frequency bands [5].

7. How Do Neurons Generate BCI Detectable Electrical Signals?

Neurons generate electrical signals through the movement of ions across their cell membranes during action potentials and synaptic activity. When a neuron receives sufficient stimulation, voltage-gated ion channels open, allowing sodium ions to rush into the cell and potassium ions to flow out. This rapid exchange of ions creates a wave of electrical depolarization that travels along the neuron’s axon as an action potential. At synapses, the arrival of an action potential triggers the release of neurotransmitters, which bind to receptors on the receiving neuron and cause ion channels to open, generating postsynaptic potentials [5, 6, 7, 8, 9, 13, 14, 16, 19, 23, 24]. BCIs can detect various types of signals including single neuron activity, local field potentials, and population-level activity when multiple neurons fire together. The systems also detect event-related desynchronization (ERD) and synchronization (ERS) patterns in neural activity [13, 16, 26]. But, the number of neurons required to generate detectable BCI signals depends heavily on the recording method – whether invasive, non-invasive, or semi-invasive [5, 6, 7, 8, 9, 13, 16, 26].

8. What Are Spontaneous & Evoked Potential Electrical Signals?

The distinction between spontaneous brain activity and evoked potentials represents a fundamental categorization in BCI signal types [8, 16, 19, 23]. Spontaneous brain activity occurs naturally without external stimulation, representing ongoing neural processes such as motor planning or attention. Evoked potentials, on the other hand, are brain responses triggered by external stimulations such as sensory, cognitive, or motor events [6, 24]. BCIs can utilize both types of activity, with spontaneous activity enabling continuous control (with motor imagery systems [17]) and evoked potentials supporting discrete selection tasks (P300 spellers depend on evoked responses to visual or auditory stimuli for communication, for example [17]) [6, 9, 19, 24].

Spontaneous Brain Activity

Spontaneous brain activity is generated voluntarily by a user without any external stimulation, arising from internal cognitive processes under conscious control [5, 6, 7, 9, 16, 17, 20, 23]. Spontaneous brain activity includes phenomena such as event-related synchronization (ERS) and event-related desynchronization (ERD) [8, 16], as well as the various brain wave patterns that fluctuate based on arousal, attention, and cognitive engagement [7], and sensorimotor rhythms that users modulate through motor imagery, slow cortical potentials that can be controlled through operant conditioning, and various cognitive tasks such as mental calculation or spatial navigation. Users actively generate these signals through specific thought patterns or mental strategies, giving them direct, voluntary control over the BCI (forming the basis for BCIs based on motor imagery and other self-generated mental activities [8]). The advantage of spontaneous signals is that users can initiate commands at will without depending on external cues [5, 6, 7, 8, 16, 19, 20].

Evoked Potentials

Evoked potentials are automatically generated brain responses triggered by external sensory stimuli, requiring no training and little conscious effort from the user, but necessitating continuous presentation of external cues [5, 6, 8, 9, 16, 17, 20]. These responses include the P300 response, which appears approximately 300 milliseconds after task-relevant “oddball stimuli” (an infrequent or significant stimulus) [5, 6, 7, 9, 16, 17, 20, 23], steady-state visual evoked potentials (SSVEPs) generated by visual stimuli [5, 6, 7, 8, 9, 16, 23], such as flickering lights or patterns [16, 17, 20], and brainstem auditory evoked potentials (BAEPs) that measure responses to auditory inputs [8]. Evoked potentials are time-locked to specific stimuli or events, making them more consistent and easier to detect initially than spontaneous signals [5, 6, 19]. These evoked responses form the basis for exogenous BCIs, which depend on external stimulation to generate control signals. While evoked potentials require no training to produce, they do require the user to attend to external stimuli continuously [5, 20].

9. How Quickly Do Signals Travel?

Neural signals travel at varying speeds depending on the type of nerve fiber and the presence of myelin insulation [5, 6]. Action potentials in myelinated axons can travel at speeds up to 120 meters per second, while unmyelinated fibers conduct signals much more slowly, at speeds of 0.5 to 2 meters per second [6, 16, 20, 24]. Cortical processing occurs remarkably quickly, with neurons capable of firing hundreds of times per second and synaptic transmission occurring in just a few milliseconds. This rapid neural signaling enables the possibility of real-time BCI control, as the brain can generate control signals quickly enough for responsive interaction with external devices [6, 9]. Research has further shown that neural activity precedes cursor velocity by approximately 150 milliseconds, meaning the brain signals encoding movement intention occur before the intended action would naturally occur. This temporal offset is crucial for real-time BCIs in that it provides a window for signal detection and processing before the intended action time [16].

10. Why Do Brain Signals Get Weaker As They Travel?

Brain signals undergo significant attenuation as they propagate from their neural sources [6, 7, 8, 9, 12, 16, 20, 23].

Multi-Layer Attenuation

Not only is the magnetic field generated by brain currents extraordinarily weak, measuring only about 100 femtoteslas, or approximately one billionth the Earth’s magnetic field strength [8], but brain signals must propagate from their neural sources through a variety of layers of biological tissue to reach external sensors [6, 7, 8, 12] – with each layer absorbing and scattering the brain’s signals, causing progressive degradation [8]. The skull, for example, is much less conductive than brain tissue or the scalp and acts as a spatial filter that preferentially attenuates higher frequency components and reduces the spatial resolution of brain signals [6, 7, 9, 16] – blurring the precise location of neural activity and making it more difficult to distinguish between signals from different brain regions [7]. 

Complex Electrical Signal Path

Beyond the challenge of the skull, the attenuation process is further complicated by the fact that different tissue layers have varying conductivities and thicknesses across individuals and even across different regions of the same person’s head [6, 7, 12, 16, 20]. The cerebrospinal fluid, meninges, skull bone, and scalp each affect signal propagation differently, for example, creating a complex path for electrical signals to traverse [6, 7, 12] and leading to signal spreading, distortion, and the limited signal-to-noise ratio that characterizes non-invasive recordings [16, 20]. This multi-layer attenuation explains why signals detected at the scalp (like EEG) represent the summed activity of large populations of neurons rather than individual neural events, and why non-invasive BCIs face fundamental challenges in achieving the same spatial resolution and signal quality as invasive systems that bypass these attenuating layers [6, 8, 9, 20]. 

11. Why Are Brain Waves & Electrical Signals The BCI Focus?

The brain operates through both electrical and chemical signaling systems that work in concert to enable neural communication [6, 7, 8, 9, 16], but BCIs focus primarily on the brain’s electrical signals (rather than chemical signals) for a variety of practical and technical reasons [6, 7, 20]. 

Electrical Signals

Electrical signals can be measured directly through a variety of methods, such as EEG, ECoG, MEG, and intracortical recordings, providing real-time, measurable, information about brain activity (with spike signals from individual neurons exhibiting frequencies exceeding 300 Hz [24]) and offering immediate responses crucial for device control [3, 5, 8, 9, 12, 16, 17, 24]. The electrical nature of neural signals also means they can be processed and analyzed using established signal processing techniques. Further, electrical signals propagate quickly and can be detected with relatively simple equipment compared to measuring chemical neurotransmitters [6, 7, 9]. 

Not Chemical Signals

In contrast, chemical signaling involves neurotransmitters that are more challenging to measure directly and continuously [24]. In addition, chemical signaling, measured through hemodynamic responses, is an indirect process where blood releases glucose to active neurons at a greater rate than in inactive areas. This hemodynamic response has inherent delays of 3 to 6 seconds, making it less suitable for real-time control applications where immediate feedback is essential [5, 7, 12, 20, 24]. As a result, nearly all BCI techniques measure electrophysiological signals. The exceptions to this electrical focus are technologies like functional near-infrared spectroscopy (fNIRS) and functional ultrasound (fUS), which measure an indirect hemodynamic readout of neuronal activity rather than direct electrical signals [16].

12. Which Parts Of The Brain Are Best For Electrical Signal Acquisition By BCIs?

Certain brain regions are particularly suitable for BCI applications due to their anatomical location (accessibility), functional organization (defined roles), and the nature of the signals they produce (robustness), with the choice of brain region significantly impacting BCI performance [6, 8, 16, 19, 24, 26]. For example, the accessibility of cortical areas near the brain’s surface makes them significantly better targets than deep brain structures, which require more invasive approaches to access [8, 17, 19, 22, 24]. Further, the cortical areas, such as the visual cortex, auditory cortex, and somatosensory cortex, serve as effective BCI targets because of their well-understood organization and specific functional roles – these regions process distinct types of information in predictable ways, making it easier to decode their signals for certain applications [8,17]. While BCIs can focus on signal acquisition from areas of the brain such as the somatosensory cortex, the prefrontal cortex, and the temporal regions, typically, the motor cortex (for motor imagery) or the visual cortex (for SSVEP) provide the most robust and easily detected signals [24].

The Motor Cortex (Frontal Lobe)

The primary purpose of the motor cortex is to generate voluntary movements by creating electrical impulses that trigger muscle contractions, plan and execute complex motor actions, and coordinate multiple muscle groups for skilled movements and maintaining posture [5, 6, 7, 8, 17, 27, 28].

Structure & Organization

The motor cortex comprises three different areas of the frontal lobe, immediately anterior to the central sulcus. These areas are the primary motor cortex (Brodmann’s area 4), the premotor cortex, and the supplementary motor area. Like all parts of the neocortex, the primary motor cortex is made of six layers. Unlike other primary sensory areas, the primary motor cortex is an agranular cortex; that is, it does not have a cell-packed granular layer (layer 4). Instead, the most distinctive layer of the primary motor cortex is its descending output layer (layer 5), which contains the giant Betz cells [28].

Purpose In BCIs

The motor cortex, located on the precentral gyrus and anterior paracentral lobule and representing the brain’s primary command center for voluntary movement [23], has become a preferred target for BCIs aimed at restoring motor function due to the cortex’s accessibility at the brain surface and its well-understood somatotopic organization (the “motor homunculus”) [1, 4, 6, 8, 9, 13, 20, 24, 27, 28, 29]. Motor areas, particularly those corresponding to hand movements at positions C3 and C4, and foot movements at position Cz, generate clear, distinct, consistent, and robust patterns of activity that can be reliably detected and interpreted during both actual movement and motor imagery, making them ideal sources of control signals [5, 6, 7, 8, 17, 27, 28]. Additionally, sensorimotor rhythms in these areas can be voluntarily modulated with training, allowing users to generate control signals at will [5].

The Prefrontal Cortex (Frontal Lobe)

The prefrontal cortex is the association cortex of the frontal lobe; it plays a cardinal role in the temporal organization of behavior and cognitive activities. The prefrontal cortex further controls the execution, order and timing of sequential acts toward a goal [81, 82], and implements control processes that contribute to working memory (WM) and episodic long-term memory (LTM) [47, 67, 68, 69, 98, 99, 100, 101, 102, 103, 104, 105].

Structure & Organization

Neuroimaging results support the idea that different prefrontal subregions differentially contribute to WM [67, 70, 71, 81, 86]. The prefrontal cortex is typically divided into three main regions: the dorsolateral prefrontal cortex (DLPFC), the medial prefrontal cortex (mPFC), and the orbitofrontal cortex (OFC) [47, 98, 99, 100, 101, 102, 103, 104, 105]. The primary focus in BCIs is the dorsolateral prefrontal cortex. Anatomically, the dorsolateral prefrontal cortex encompasses the cortex on the dorsolateral surface of the frontal lobe, including the superior, middle, and inferior frontal gyri, and is composed of six-layered, well-differentiated cortex in humans. It is central to executive functions [72, 81, 82, 83, 84, 85, 91, 92, 93, 94, 95, 96, 97, 98] such as: abstract reasoning [73], response inhibition [74], planning [75], cognitive flexibility [76], working memory (WM) [77], organization, set shifting, and selective attention [98, 100, 102, 106, 107, 108, 109, 110, 111, 112, 113]. But, treating the DLPFC as a singular region likely amounts to a gross oversimplification [72]. Even at a purely cytoarchitectonic level, the DLPFC comprises at least two subregions—Brodmann area 9 (BA9) and BA46 [78]. Subdivisions within the DLPFC receive inputs from distinct regions throughout the brain, providing further evidence for a more parcellated anatomic organization [72, 79, 80].

Purpose In BCIs

The prefrontal cortex offers an accessible implantation site for decoding motor intentions compared to deeper brain structures [22] and is an important target for P300-based BCIs [23]. The dorsolateral prefrontal cortex, in particular, has proven useful as an alternative target when sensorimotor signals are unavailable, providing flexibility in implant location [13], and BCI users have reported dorsolateral prefrontal cortex (dlPFC)-based BCI control to be less mentally taxing than sensorimotor-based control [13, 63, 64]. Further, as cognitive control involved in sustained attention tasks is correlated with frontal midline theta-band activity [62, 63, 64, 65, 66], frontal theta has become a mechanism by which emergent processes may be biophysically realized [63] – non-invasive brain stimulation techniques such as rTMS and tDCS targeting the DLPFC have demonstrated efficacy in modulating cortical excitability, functional connectivity, and clinical symptoms in depression, addiction, chronic pain, schizophrenia, and cognitive impairment [81, 87, 88, 89]. Interestingly, and perhaps vital for future applications, studies have shown that dopaminergic and cholinergic modulators can increase DLPFC neuroplasticity [81, 90].

The Visual Cortex (Occipital Lobe)

The primary purpose of the visual cortex is to receive, segment, and integrate visual information [34, 36]. 

Structure & Organization

The visual cortex, located at the back of the brain, is one of the best understood areas of the cerebral cortex; it constitutes a prime workbench for the study of cortical circuits and of computations [31, 32, 33, 34, 35]. Numerous factors contribute to this fortunate position: we understand the nature of its main inputs, we know what stimuli make its neurons fire, and we can easily make those stimuli thanks to computer displays [8, 31]. The visual cortex divides into five different areas (V1 to V5) based on function and structure. V1, also known as the primary visual cortex, centers around the calcarine sulcus and is divided into six distinct layers, each comprising different cell types and functions [34, 35]. V1 neurons respond to spatial orientation, direction of motion, temporal frequency, binocular depth, and color [31, 34, 37, 38, 39].

Purpose In BCIs

The location of the visual cortex at the back of the brain (occipital lobe [34, 35]) makes it accessible via non-invasive, scalp EEG recording, and the cortex’s robust responses to flickering stimuli enable high information transfer rates [16, 25, 31]. The visual cortex’s combination of anatomical accessibility and functional specificity makes the cortex a commonly targeted region for BCIs, particularly for BCIs using visual evoked potentials (VEPs) [5, 8, 17, 23]. The visual cortex’s response to specific stimulation frequencies makes the region ideal for SSVEP-based BCIs that can offer multiple control options simultaneously [5, 17, 23].

The Somatosensory Cortex (Parietal Lobe)

Adjacent to the motor cortex, the somatosensory system is concerned with input from different submodalities such as touch, proprioception, and sensitivity to hot and cold, pain and itch [40, 42, 44, 50, 57]. During voluntary movement, the somatosensory system not only passively receives signals from the external world, but also actively processes them via interactions with the motor system [58, 59, 61].

Structure & Organization

The primary somatosensory cortex is on the postcentral gyrus [40, 41, 44] and is a primary receptor of general bodily sensation [40, 42]. Thalamic radiations relay sensory data from skin, muscles, tendons, and joints of the body to the primary somatosensory cortex (SI), a functional region consisting of Brodmann’s areas 3, 1, and 2 of the postcentral gyrus [40, 42, 43, 46, 50, 57]. The surface of the body maps onto the surface of the brain [40], and S1 is characterized by a highly organized somatotopic representation of the body surface, often referred to as the sensory homunculus (“little man”), in which adjacent cortical regions correspond to adjacent areas of the body [40, 42, 50, 51, 57].

Purpose In BCIs

Functionally defined areas like the somatosensory cortex serve as effective BCI targets due to their well-understood organization and specific functional roles [8]. Studies have shown that neural activity can be measured from the central areas of the primary sensorimotor cortex by both EEG [30] and MEG [30, 60], and that fMRI has high enough sensitivity and specificity to even characterize the somatotopic map of each individual finger and fingertip [51, 52, 53, 54, 55, 56, 57]. Finally, FES-based BCIs can activate the somatosensory cortex involved in the motor neural control loop, allowing functional movement of paralyzed limbs [9, 48, 49]. 

The Auditory Cortex (Temporal Lobe)

The ultimate target of afferent auditory information is the auditory cortex [129, 131, 134] – its primary function is to receive and interpret sound signals from the ears [134]. The auditory cortex is responsible for analyzing and categorizing (processing) different sound frequencies and patterns [134] – it decodes the spectral and temporal information embedded in sounds and forms a neural representation of them [131] –  allowing individuals to both perceive and recognize different types of sounds, pitch, loudness, and timbre, as well as prosody (rhythm, intonation, and stress) that conveys meaning and emotion [131, 134].

Structure & Organization

The primary auditory cortex (also called A1), located within the temporal lobes in both hemispheres of the brain, is tonotopically organized like the other structures in the central auditory pathway [119, 129, 134]. Similar to the visual cortex, the auditory cortex is made up of six layers of cells in columnar organization. Interestingly, each cortical column responds to a specific frequency [119], and studies have shown that different regions of the auditory cortex are specialized for processing different aspects of music, such as melody processing in the right superior temporal gyrus and rhythm processing in the left inferior frontal gyrus [134]. Although the auditory cortex has a number of subdivisions [131, 134], a broad distinction can be made between a primary area and peripheral, or belt, areas. The belt areas of the auditory cortex receive more diffuse input from the belt areas of the medial geniculate complex and therefore are less precise in their tonotopic organization [130]. The primary auditory cortex (A1) has a topographical map of the cochlea, just as the primary visual cortex (V1) and the primary somatic sensory cortex (S1) have topographical maps of their respective sensory epithelia [129, 130].

Purpose In BCIs

Auditory brain–computer interfaces (BCIs) enable users to select commands based on the brain activity elicited by auditory stimuli [120, 121, 122, 123, 124, 132]. Auditory feedback, and specifically musical auditory feedback, offers several tangible and potential advantages over visual feedback [133]: (1) auditory feedback can be used in patients with visual impairment who might not be able to operate a visual feedback-based BCI [135], (2) auditory feedback may facilitate a stronger learning effect [136, 137, 138] and (3) auditory (and particularly musical) feedback may be more motivating for more sustained attention [133, 137, 139]. In particular, Lopez et al. [125] and Kim et al. [126] proposed ASSR-based auditory BCIs [121], P300 spellers using auditory stimuli were tested by Furdea et al. [127], Markovinović et al. proposed an auditory speller with a convolutional neural network (CNN) [121, 128], and Paradromics’ Connexus uses electrodes to capture signals from this and other areas of the temporal lobe [4].

13. How Does The Brain’s Plasticity Affect BCI Signal Capture & Interpretation?

Brain plasticity, the nervous system’s remarkable capacity to form new neural connections and reorganize existing ones, plays a crucial role in learning [2, 7] and BCI performance [3, 4, 5, 6, 7, 9, 14, 16, 17, 19, 20, 23, 24]. Interestingly, research shows that performance often plateaus after initial learning, typically within one to three sessions. This rapid plateau suggests that the brain quickly finds efficient strategies for BCI control, particularly when the mental task naturally corresponds to the desired output [19].

Plasticity Enables Bidirectional Co-Evolution Of Man & Machine

BCIs leverage the brain’s plasticity [7, 8, 16, 19, 24], allowing both user and system to adapt to one another in a unique form of learning in which both the biological and artificial components evolve together to actually improve performance and optimize control strategies [3, 6, 7, 8, 16, 17, 19, 24]. This bidirectional learning process involves, for the user, changes at multiple levels of neural organization, from modifications in local cortical circuits to reorganization of large-scale networks [24]. Bidirectional adaptation between brain and machine allows the brain to learn to produce clearer, more consistent control signals associated with successful BCI commands, at the same time that the BCI system prunes less effective patterns and adapts to the user’s evolving neural signals [4, 5, 6, 7, 8, 10, 16, 17, 19, 20, 24],

Plasticity Enables Intuitive & Adaptive Device Control Strategies

Over time, users report that control becomes more intuitive and less fatiguing, similar to how complex motor skills become automatic with practice [4, 5, 6, 14, 16, 17, 24]; some users develop highly efficient neural patterns that allow for intuitive device control with minimal conscious effort [7, 9, 16]. Amazingly, plasticity allows for the development of entirely new neural patterns dedicated to BCI device control, patterns distinct from those used for attempted physical movement – essentially learning a new form of highly-personalized output that bypasses damaged motor pathways in order to restore function [3, 5, 6, 7, 8, 14, 16, 17, 24]. Plasticity also enables recovery and adaptation after changes in neural signal recording conditions. When electrode positions shift or signal quality degrades, users can often adapt their control strategies to maintain performance [5, 6]. This adaptive capability has been particularly important in invasive BCIs, where signal characteristics may change over months due to tissue reactions. The brain’s plasticity allows users to compensate for these changes, maintaining control even as the underlying signals evolve [5]

Plasticity Challenges BCI Systems – The Need For Recalibration

However, plasticity also presents challenges for BCI systems. As users become more proficient, their brain patterns and control strategies evolve, shifting away from those the system was initially trained to recognize – creating a challenge for BCI classification algorithms and requiring periodic recalibration in order to maintain stable control [2, 5, 6, 7, 8, 9, 12, 13, 16, 17, 18, 19, 20].

14. Final Thoughts

We stand at the beginning of a revolution that could restore mobility to the paralyzed, voice to the non-verbal, and, eventually, enhance human capabilities beyond current limitations. While significant challenges remain – from regulatory hurdles and high costs to technical limitations and ethical concerns – progress from companies like Neuralink and Paradromics demonstrates that BCIs have transitioned past science fiction to clinical reality.

Thanks for reading!

15. Appendix

Chronology

• 1875 – Dr. Richard Caton observed electric currents in the grey matter of rabbits and monkeys using a galvanometer, marking one of the first observations of its kind [12]
• 1913 – Vladimir Vladimirovich Pravdich-Neminsky produced the first photograph of potentials inside an animal’s brain [12]
• 1920s – Hans Berger achieved a seminal milestone in neuroscience by recording the first electroencephalogram (EEG), marking the inception of a scientific method for monitoring human brain activity [9, 12, 22, 23, 24]. Hans Berger discovered alpha and beta oscillation frequencies in the brain [12]
• 1929 – The earliest simultaneous neural recording traces back to Matthews – from the frog peroneal nerve [22] 
• 1930s – Wilder Penfield demonstrated that targeted brain stimulation could elicit a desire to move [18]
• 1947 – The introduction of the widely-used 10-20 system for EEG measurements, which refers to the placement of electrodes on the scalp, each being either 10 or 20 percent of the scalp’s distance away from one another, allowing for comprehensive coverage of the different brain lobes [12]
• 1949 – Donald O. Hebb first illustrated the plastic characteristics of the brain [3]
• 1960s – David Rosenboom began experimenting with linking brain functions to musical production [3]
• 1964 – W.G. Walter et al. first observed direct current shifts [12]
• 1973 – The concept of a brain-computer interface “BCI” was first articulated by Jacques Vidal [2, 3, 8, 9, 12, 23, 24]
• 1986 – Rumelhart and McClelland introduced the multilayer perceptron (MLP), one of the most well-known artificial neural network structures that would later be used extensively in BCIs [5]
• 1988 – Farwell and Donchin designed one of the first P300 spellers, which allowed users to spell characters by focusing attention on a 6×6 matrix with randomly flashing rows and columns [3, 5, 9, 23, 27]. This groundbreaking system achieved a maximum accuracy of 95% and a speed of 12 bits per minute, establishing the foundation for future BCI communication systems [3, 5, 6]. Stevo Bozinovski and colleagues reported using EEG alpha waves to control a mobile robot, marking the first successful attempt at utilizing brainwaves to control a robot [23]
• 1991 – The first attempt to control a cursor using EEG signals was described by Wolpaw and colleagues, who demonstrated that vertical movement of a cursor on a video screen could be controlled by changing mu-rhythm amplitude [6, 12]. In their system, large amplitude mu-rhythms moved the cursor upward, while small amplitudes moved it downward, establishing the fundamental principle that voluntary modulation of brain rhythms could be used for device control [6]
• 1992 – Erich E. Sutter proposed a BCI system based on visually evoked potentials. Sutter designed an 8×8 speller that utilized visual evoked potentials collected from the brain’s visual cortex to determine the direction of the user’s gaze and thereby identify the symbols selected on the speller [23]
• 1993 – McFarland and colleagues conducted cursor control experiments that achieved a 54.85% target hit rate, further validating the potential of BCIs for practical applications [6]
• 1997 – Deep brain stimulation (DBS) was approved for commercial use in the US [114]
• 1998 – The first human BCI implant was performed by P.R. Kennedy and R.A. Bakay (in a paralyzed woman, who after many months of training was able to send on/off signals) [12, 18]
• 1999 – Guger and colleagues pioneered EEG-based prosthetic control systems that utilized binary output signals derived from imagined hand movements. Their work demonstrated that users could control external devices by classifying EEG patterns generated during the imagination of left- and right-hand movements, using minimal electrode setups. Chapin and colleagues demonstrated real-time control of robotic arms using recorded neural activity. Their approach incorporated sophisticated linear decoders and Kalman filters to map neural signals to desired arm movements, achieving high accuracy and low latency in translating neural intentions into robotic actions [20]
• 2003 – Harrison invented the capacitively coupled instrumental amplifier (CCIA) for neuro acquisition, which represented a major advancement in the field’s technical capabilities [22]
• 2004 – A quadriplegic man received an implant allowing him to move a cursor, switch lights and TV channels, and read emails [12]
• 2005 – Lal et al. presented the first online MEG-based BCI, expanding the range of neuroimaging modalities available for brain-computer interfaces [5]. Tanaka et al. presented the first application of wheelchair control using only EEG, demonstrating that BCIs could provide mobility solutions for severely disabled individuals [5, 23]
• 2006 – Hochberg et al. demonstrated the neural cursor system [27]. The first MI-based BCI, named Hex-o-Spell, was introduced [9]. Researchers using electrocorticography achieved 73-100% success rates in one-dimensional cursor motions, demonstrating that less invasive approaches could still achieve high accuracy [12]
• 2012 – Leigh R. Hochberg et al. demonstrated tetraplegic patients controlling a robotic hand via implanted 96-channel microelectrode arrays inserted into their motor cortex area responsible for hand movements [12]. Researchers redefined BCI as “a new non-muscular channel” for interaction, emphasizing its role in providing alternative communication and control methods for those with motor disabilities [23]
• 2016 – Oxley and colleagues introduced the Stentrode stent-electrode recording array, representing a novel minimally invasive approach to brain signal recording [24]
• 2017 – Tetraplegia patients achieved 80-100% success rates in grasping and movement targets through combined cortical implants and muscle stimulation showed how BCIs could restore near-normal function [12]
• 2019 – Guan and colleagues proposing the Neural Matrix using flexible silicon film transistors. Researchers successfully converted neural activity directly into speech using ECoG signals, representing a breakthrough in decoding complex neural patterns [24]
• 2020 – Neuralink’s public demonstration showing real-time wireless recording from 1,024 electrodes, proving the feasibility of high-bandwidth wireless BCIs for complex robot control [1]
• 2022 – Synchron made history with the first US implant of their Stentrode device in an ALS patient [1, 2, 4]. Willett and colleagues employed the BrainGate system to achieve brain-to-text communication by interpreting handwriting, demonstrating the potential for BCIs to restore complex communication abilities [24, 27]. BrainCo’s BrainRobotics intelligent bionic hand received FDA approval for market release, representing a major advancement in non-invasive BCI applications [11]
• 2023 – Neuralink received FDA approval (after a rejection in 2022) for human clinical trials [4, 10]
• 2024 – Neuralink’s experimental Blindsight implant received “breakthrough device” status from the U.S. Food and Drug Administration (FDA). Neuralink implanted an electronic device, called Telepathy N1, into the brain of a disabled individual [10]. Paradromics Inc. announced its acceptance into the U.S. Food and Drug Administration’s newest program for innovative devices, the Total Product Life Cycle Advisory Program [117]. Precision Neuroscience received FDA 510(k) clearance for its Layer 7 Cortical Interface implant [4]
• 2025 – Paradromics Inc. announced the successful completion of its first-in-human procedure with the Connexus® Brain-Computer Interface (BCI). This surgery marked the beginning of clinical efforts for Paradromics [4, 118]

References

[1] Colwell, Brian – Brain-Computer Interfaces (BCIs): The Next Frontier In Robot Control – https://briandcolwell.com/brain-computer-interfaces-bcis-the-next-frontier-in-robot-control/

[2] Maiseli, et al. – Brain–computer interface: trend, challenges, and threats – https://braininformatics.springeropen.com/articles/10.1186/s40708-023-00199-3

[3] Saha, et al. – Progress in Brain Computer Interface: Challenges and Opportunities – https://www.frontiersin.org/journals/systems-neuroscience/articles/10.3389/fnsys.2021.578875/full

[4] Becher, Brooke – Brain Computer Interfaces (BCI), Explained – https://builtin.com/hardware/brain-computer-interface-bci

[5] Nicolas-Alonso & Gomez-Gil – Brain Computer Interfaces, a Review – https://pmc.ncbi.nlm.nih.gov/articles/PMC3304110/

[6] Rashid, et al. – Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review – https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2020.00025/full

[7] Zhang, et al. – Brain–computer interfaces: the innovative key to unlocking neurological conditions – https://pmc.ncbi.nlm.nih.gov/articles/PMC11392146/

[8] Liao, et al. – Exploring the Intersection of Brain–Computer Interfaces and Quantum Sensing: A Review of Research Progress and Future Trends – https://www.researchgate.net/publication/375795776_Exploring_the_Intersection_of_Brain-Computer_Interfaces_and_Quantum_Sensing_A_Review_of_Research_Progress_and_Future_Trends

[9] Liu, et al. – Recent applications of EEG-based brain-computer-interface in the medical field – https://mmrjournal.biomedcentral.com/articles/10.1186/s40779-025-00598-z

[10] Lavazza, et al. – Neuralink’s brain-computer interfaces: medical innovations and ethical challenges – https://www.frontiersin.org/journals/human-dynamics/articles/10.3389/fhumd.2025.1553905/full

[11] Yang, Jinyi – Challenges and Trends in Brain-Computer Interface Technology – https://www.ewadirect.com/proceedings/ace/article/view/23006

[12] Caiado, et al. – The history, current state and future possibilities of the non-invasive brain computer interfaces – https://www.sciencedirect.com/science/article/pii/S2590093525000049

[13] Khan, et al. – Invasive Brain–Computer Interface for Communication: A Scoping Review – https://www.mdpi.com/2076-3425/15/4/336

[14] Gouret, et al. – Advancements in brain-computer interfaces for the rehabilitation of unilateral spatial neglect: a concise review – https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2024.1373377/full

[15] Lim, et al. – The state-of-the-art of invasive brain-computer interfaces in humans: a systematic review and individual patient meta-analysis – https://pubmed.ncbi.nlm.nih.gov/39978072/

[16] Edelman, et al. – Non-Invasive Brain-Computer Interfaces: State of the Art and Trends – https://pmc.ncbi.nlm.nih.gov/articles/PMC11861396/

[17] Peksa, et al. – State-of-the-Art on Brain-Computer Interface Technology – https://www.mdpi.com/1424-8220/23/13/6001

[18] Gordon & Seth – Ethical considerations for the use of brain–computer interfaces for cognitive enhancement – https://pmc.ncbi.nlm.nih.gov/articles/PMC11542783/

[19] Ding, et al. – EEG-based brain-computer interface enables real-time robotic hand control at individual finger level – https://www.nature.com/articles/s41467-025-61064-x

[20] Cruz, et al. – A Comprehensive Survey of Brain–Computer Interface Technology in Health care: Research Perspectives – https://pmc.ncbi.nlm.nih.gov/articles/PMC12180781/

[21] Deng, et al. – Application and future directions of brain-computer interfaces in neurological disorders: Technological advances, clinical practices, and challenges – https://www.sciencedirect.com/science/article/pii/S2589238X2500083X

[22] Chen, et al. – Brain–computer interfaces in 2023–2024 – https://www.researchgate.net/publication/390335479_Brain-computer_interfaces_in_2023-2024

[23] Wang, et al. – A Review of Brain-Computer Interface Technologies: Signal Acquisition Methods and Interaction Paradigms – https://arxiv.org/html/2503.16471v1

[24] Sun, et al. – Signal acquisition of brain–computer interfaces: A medical-engineering crossover perspective review – https://www.sciencedirect.com/science/article/pii/S2667325824001559

[25] Neuralink – Building Safe Implantable Devices – https://neuralink.com/updates/building-safe-implantable-devices/

[26] Paradromics – Electrocorticography (ECoG) surveys the landscape – https://www.paradromics.com/blog/electrocorticography

[27] Paradromics – Augmentative and alternative communication BCIs – https://www.paradromics.com/blog/augmentative-alternative-communication

[28] Knierim, et al. – Chapter 3: Motor Cortex – https://nba.uth.tmc.edu/neuroscience/m/s3/chapter03.html

[29] Schieber, M. H. – Constraints on somatotopic organization in the primary motor cortex – https://pubmed.ncbi.nlm.nih.gov/11698506/

[30] Inamoto, et al. – Motor-Related Mu/Beta Rhythm in Older Adults: A Comprehensive Review – https://pmc.ncbi.nlm.nih.gov/articles/PMC10216152/

[31] Scholarpedia – Area V1 – http://www.scholarpedia.org/article/Area_V1

[32] Schmolesky, M. – Webvision: The Organization of the Retina and Visual System – https://www.ncbi.nlm.nih.gov/books/NBK11524/

[33] Schmolesky, M. – THE PRIMARY VISUAL CORTEX – https://www.webvision.pitt.edu/book/part-ix-brain-visual-areas/the-primary-visual-cortex/

[34] Huff, et al. – Neuroanatomy, Visual Cortex – https://www.ncbi.nlm.nih.gov/books/NBK482504/

[35] Tran, et al. –  Neuronal mechanisms of motion detection underlying blindsight assessed by functional magnetic resonance imaging (fMRI) – https://pubmed.ncbi.nlm.nih.gov/30825453/

[36] Goodale M. – Transforming vision into action – https://pubmed.ncbi.nlm.nih.gov/20691202/

[37] DeAngelis, et al. – Receptive-field dynamics in the central visual pathways – https://pubmed.ncbi.nlm.nih.gov/8545912/

[38] Anzai, et al. – Neurons in monkey visual area V2 encode combinations of orientations – https://pubmed.ncbi.nlm.nih.gov/17873872/

[39] Fournier, et al – Spatial Information in a Non-retinotopic Visual Cortex – https://pubmed.ncbi.nlm.nih.gov/29249282/

[40] Webb, W. – Primary Somatosensory Cortex: Organization of the Nervous System I – https://www.sciencedirect.com/topics/immunology-and-microbiology/primary-somatosensory-cortex

[41] Bentivoglio & Swanson – Gyri: On the fine structure of the (with XIII-XXIII) – https://www.sciencedirect.com/topics/neuroscience/gyri

[42] ScienceDirect – Somatosensory Cortex – https://www.sciencedirect.com/topics/neuroscience/somatosensory-cortex

[43] ScienceDirect – Postcentral Gyrus – https://www.sciencedirect.com/topics/neuroscience/postcentral-gyrus

[44] ScienceDirect – Parietal Lobe – https://www.sciencedirect.com/topics/neuroscience/parietal-lobe

[45] ScienceDirect – Thalamus –  https://www.sciencedirect.com/topics/neuroscience/thalamus

[46] ScienceDirect – Brodmann Area – https://www.sciencedirect.com/topics/neuroscience/brodmann-area

[47] Manohar & Husain – Working Memory for Sequences of Temporal Durations Reveals a Volatile Single-Item Store – https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2016.01655/full

[48] Fadli, et al. – Effectiveness of motor and prefrontal cortical areas for brain-controlled functional electrical stimulation neuromodulation – https://iopscience.iop.org/article/10.1088/1741-2552/acfa22

[49] Behboodi, et al. – Development and evaluation of a BCI-neurofeedback system with real-time EEG detection and electrical stimulation assistance during motor attempt for neurorehabilitation of children with cerebral palsy – https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2024.1346050/full

[50] BioSourcesSoftware – What Are Brodmann Areas? – https://www.biosourcesoftware.com/brodmann-areas

[51] Dall’Orso, et al. – Somatotopic Mapping of the Developing Sensorimotor Cortex in the Preterm Human Brain – https://pmc.ncbi.nlm.nih.gov/articles/PMC5998947/

[52] Schweizer, et al. – Finger representations in human primary somatosensory cortex as revealed by high-resolution functional MRI of tactile stimulation – https://www.sciencedirect.com/science/article/abs/pii/S1053811908005168?via%3Dihub

[53] Martuzzi, et al. – Human finger somatotopy in areas 3b, 1, and 2: a 7T fMRI study using a natural stimulus – https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.22172

[54] Arichi, et al. – Somatosensory cortical activation identified by functional MRI in preterm and term infants – https://www.sciencedirect.com/science/article/abs/pii/S1053811909011094?via%3Dihub

[55] Arichi, et al. – Development of BOLD signal hemodynamic responses in the human brain – https://www.sciencedirect.com/science/article/pii/S1053811912006702?via%3Dihub

[56] Allievi, et al. – Maturation of sensori-motor functional responses in the preterm brain – https://academic.oup.com/cercor/article/26/1/402/2367359

[57] Schellekens, et al. – A touch of hierarchy: population receptive fields reveal fingertip integration in Brodmann areas in human primary somatosensory cortex – https://pubmed.ncbi.nlm.nih.gov/34091731/

[58] Cardellicchio , et al. – Beta Rebound as an Index of Temporal Integration of Somatosensory and Motor Signals – https://pmc.ncbi.nlm.nih.gov/articles/PMC7492746/

[59] Umeda, et al. – The somatosensory cortex receives information about motor output – https://www.science.org/doi/10.1126/sciadv.aaw5388

[60] Ahn, et al. – Steady-State Somatosensory Evoked Potential for Brain-Computer Interface—Present and Future – https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2015.00716/full

[61] Cardellicchio, et al. – Beta Rebound as an Index of Temporal Integration of Somatosensory and Motor Signals – https://www.frontiersin.org/journals/systems-neuroscience/articles/10.3389/fnsys.2020.00063/full

[62] Gaume, et al. – A cognitive brain–computer interface monitoring sustained attentional variations during a continuous task – https://pmc.ncbi.nlm.nih.gov/articles/PMC6520431/

[63] Cavanagh, et al. – Frontal theta as a mechanism for cognitive control – https://www.cell.com/trends/cognitive-sciences/abstract/S1364-6613(14)00107-7

[64] Clayton, et al. – The roles of cortical oscillations in sustained attention – https://pubmed.ncbi.nlm.nih.gov/25765608/

[65] Wokke, et al. – Sure i’m sure: prefrontal oscillations support metacognitive monitoring of decision making – https://pubmed.ncbi.nlm.nih.gov/28123015/

[66] Eisma, et al. – Frontal midline theta differentiates separate cognitive control strategies while still generalizing the need for cognitive control – https://pubmed.ncbi.nlm.nih.gov/34282209/

[67] Blumenfeld & Ranganath – Dorsolateral Prefrontal Cortex Promotes Long-Term Memory Formation through Its Role in Working Memory Organization – https://pmc.ncbi.nlm.nih.gov/articles/PMC6675388/

[68] Shimamura, et al. – Memory and the prefrontal cortex – https://psycnet.apa.org/record/1996-97403-010

[69] Ranganath & Knight – Prefrontal cortex and episodic memory: integrating findings from neuropsychology and event-related functional neuroimaging – https://www.researchgate.net/publication/228606986_Prefrontal_cortex_and_episodic_memory_Integrating_findings_from_neuropsychology_and_functional_brain_imaging

[70] D’Esposito et al. – Maintenance versus manipulation of information held in working memory: an event-related fMRI study – https://pubmed.ncbi.nlm.nih.gov/10536086/

[71] Petrides, M. – The role of the mid-dorsolateral prefrontal cortex in working memory – https://pubmed.ncbi.nlm.nih.gov/10933209/

[72] Ahuja & Rodriguez – Is the Dorsolateral Prefrontal Cortex Actually Several Different Brain Areas? – https://www.jneurosci.org/content/42/33/6310

[73] Bernardi et al. – The Geometry of Abstraction in the Hippocampus and Prefrontal Cortex – https://pubmed.ncbi.nlm.nih.gov/33058757/

[74] Blasi et al. – Brain regions underlying response inhibition and interference monitoring and suppression – https://pubmed.ncbi.nlm.nih.gov/16553630/

[75] Crescentini et al. – Two networks involved in producing and realizing plans – https://pubmed.ncbi.nlm.nih.gov/22433287/

[76] Badre et al. – The dimensionality of neural representations for control – https://pubmed.ncbi.nlm.nih.gov/32864401/

[77] Warden & Miller – Task-Dependent Changes in Short-Term Memory in the Prefrontal Cortex – https://www.jneurosci.org/content/30/47/15801

[78] Petrides, M. – Lateral prefrontal cortex: architectonic and functional organization – https://pubmed.ncbi.nlm.nih.gov/15937012/

[79] Petrides & Pandya – Projections to the frontal cortex from the posterior parietal region in the rhesus monkey – https://pubmed.ncbi.nlm.nih.gov/6480903/

[80] Thiebaut de Schotten, et al. – Monkey to human comparative anatomy of the frontal lobe association tracts – https://pubmed.ncbi.nlm.nih.gov/22088488/

[81] ScienceDirect – Dorsolateral Prefrontal Cortex – https://www.sciencedirect.com/topics/neuroscience/dorsolateral-prefrontal-cortex

[82] ScienceDirect – Prefrontal Cortex – https://www.sciencedirect.com/science/article/abs/pii/B0080430767034653

[83] Fuster, J.M. – Prefrontal Cortex – https://www.sciencedirect.com/science/article/abs/pii/B0080430767034653

[84] Scott & Bourne – Modelling behaviors relevant to brain disorders in the nonhuman primate: Are we there yet? – https://www.sciencedirect.com/science/article/abs/pii/S0301008221001970

[85] Millett, et al. – Do group-based mindfulness meditation programs enhance executive functioning? A systematic review and meta-analysis of the evidence – https://www.sciencedirect.com/science/article/abs/pii/S1053810021001215

[86] Karunakaran, et al. – NIRS measures in pain and analgesia: Fundamentals, features, and function – https://www.sciencedirect.com/science/article/abs/pii/S0149763420306278

[87] Downar & Daskalakis – New Targets for rTMS in Depression: A Review of Convergent Evidence – https://www.sciencedirect.com/science/article/abs/pii/S1935861X12001544

[88] Reis, et al. – Interventions for deficits in recognition of emotions in facial expressions in major depressive disorder: An updated systematic review of clinical trials – https://www.sciencedirect.com/science/article/abs/pii/S0149763423003366

[89] Dinkelbach, et al. – Non-invasive brain stimulation in Parkinson’s disease: Exploiting crossroads of cognition and mood – https://www.sciencedirect.com/science/article/abs/pii/S0149763416304316

[90] Tremblay, et al. – Clinical utility and prospective of TMS–EEG – https://www.sciencedirect.com/science/article/abs/pii/S138824571930001X

[91] Holt, et al. – Neuroanatomical Systems Relevant to Neuropsychiatric Disorders – https://www.sciencedirect.com/science/article/abs/pii/B9780443118449000734

[92] Feil, et al. – Addiction, compulsive drug seeking, and the role of frontostriatal mechanisms in regulating inhibitory control – https://www.sciencedirect.com/science/article/abs/pii/S0149763410000461

[93] Zgaljardic, et al. – Executive Dysfunction – https://www.sciencedirect.com/science/article/abs/pii/B9780123741059001763

[94] Meijers, et al. – Prison and the brain – https://www.sciencedirect.com/science/article/abs/pii/B9780128213759000128

[95] Padberg & Burkhardt – Interventional therapeutics in psychiatry: Beyond psychopharmacology and psychotherapy? – https://www.sciencedirect.com/science/article/abs/pii/B978044318496300001X

[96] Jog, et al. – A review of transcranial direct current stimulation (tDCS) for the individualized treatment of depressive symptoms – https://www.sciencedirect.com/science/article/abs/pii/S2468171719300031

[97] Kearney-Ramos & Haney – Repetitive transcranial magnetic stimulation as a potential treatment approach for cannabis use disorder – https://www.sciencedirect.com/science/article/abs/pii/S027858462100049X

[98] Mirino, et al. – Role of the dorsolateral prefrontal cortex in processing temporal anomalies retained in working memory – https://www.frontiersin.org/journals/behavioral-neuroscience/articles/10.3389/fnbeh.2024.1494227/full

[99] Baddeley & Hitch – Working Memory – https://www.sciencedirect.com/science/article/abs/pii/S0079742108604521

[100] Ferrandez et al. – Basal ganglia and supplementary motor area subtend duration perception: an fMRI study – https://www.sciencedirect.com/science/article/abs/pii/S1053811903001599?via%3Dihub

[101] Pan & Luo – Working memory modulates the perception of time – https://link.springer.com/article/10.3758/s13423-011-0188-4

[102] Üstün et al. – Neural Networks for Time Perception and Working Memory – https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2017.00083/full

[103] Liu et al. – ​​Specificity of Inferior Olive Response to Stimulus Timing – https://journals.physiology.org/doi/full/10.1152/jn.00961.2007

[104] Manohar & Husain – Human ventromedial prefrontal lesions alter incentivisation by reward – https://www.sciencedirect.com/science/article/pii/S0010945216000083

[105] Wu, et al. – Inferior Olive Response to Passive Tactile and Visual Stimulation with Variable Interstimulus Intervals – https://link.springer.com/article/10.1007/s12311-010-0203-8

[106] Kastner & Ungerleider – Mechanisms of Visual Attention in the Human Cortex – https://www.annualreviews.org/content/journals/10.1146/annurev.neuro.23.1.315

[107] Pecchinenda et al. – Modulation of selective attention by polarity-specific tDCS effects – https://www.sciencedirect.com/science/article/abs/pii/S0028393214004837?via%3Dihub

[108] Sdoia et al. – Access to consciousness of briefly presented visual events is modulated by transcranial direct current stimulation of left dorsolateral prefrontal cortex – https://www.nature.com/articles/s41598-019-47527-4

[109] Zanto et al. – Fronto-parietal network: flexible hub of cognitive control – https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613(13)00222-2

[110] Lewis & Miall – A right hemispheric prefrontal system for cognitive time measurement – https://www.sciencedirect.com/science/article/abs/pii/S037663570500269X?via%3Dihub

[111] Zanto, et al. – Causal role of the prefrontal cortex in top-down modulation of visual processing and working memory – https://www.nature.com/articles/nn.2773

[112] Allman & Meck – Pathophysiological distortions in time perception and timed performance – https://academic.oup.com/brain/article-abstract/135/3/656/263693

[113] Allman, et al. – Time in the Psychopathological Mind – https://www.researchgate.net/publication/259976298_Time_in_the_psychopathological_mind

[114] Paradromics – Brain implants and the future of healthcare – https://www.paradromics.com/blog/bci-tech

[115] Paradromics – Neuroengineering the brain – https://www.paradromics.com/blog/neuroengineering-the-brain

[116] Paradromics – Neurotech that lasts: Building the next-generation of brain interface technology – https://www.paradromics.com/blog/neurotech-that-lasts

[117] Paradromics – Paradromics Accepted to FDA Regulatory Accelerator Program and Announces New Patient Registry – https://www.paradromics.com/news/paradromics-accepted-to-fda-regulatory-accelerator-program-and-announces-new-patient-registry

[118] Paradromics – Paradromics Completes First-In-Human Recording with the Connexus® Brain-Computer Interface – https://www.paradromics.com/news/paradromics-completes-first-in-human-recording-with-the-connexus-brain-computer-interface 

[119] Michigan State University – AUDITORY SYSTEM: CENTRAL PROCESSING – https://openbooks.lib.msu.edu/introneuroscience1/chapter/central-auditory-processing

[120] Ogino, et al. – Simultaneous multiple-stimulus auditory brain–computer interface with semi-supervised learning and prior probability distribution tuning – https://iopscience.iop.org/article/10.1088/1741-2552/

[121] Kojima & Kanoh – An auditory brain-computer interface based on selective attention to multiple tone streams – https://journals.plos.org/plosone/article?id=10.1371/

[122] Gao, et al. – An auditory brain-computer interface using virtual sound field – https://pubmed.ncbi.nlm.nih.gov/22255354/

[123] ​​Hill, et al. – An Auditory Paradigm for Brain-Computer Interfaces – https://www.researchgate.net/publication/221619526_An_Auditory_Paradigm_for_Brain–Computer_Interfaces

[124] Schreuder, et al. – A New Auditory Multi-Class Brain-Computer Interface Paradigm – https://pubmed.ncbi.nlm.nih.gov/20368976/

[125] Lopez, et al. – Evidences of cognitive effects over auditory steady-state responses by means of artificial neural networks and its use in brain–computer interfaces – https://www.sciencedirect.com/science/article/abs/pii/S0925231209002045?via%3Dihub

[126] Kim, et al. – Classification of selective attention to auditory stimuli: Toward vision-free brain–computer interfacing – https://www.sciencedirect.com/science/article/abs/pii/S0165027011000823?via%3Dihub

[127] Furdea, et al. – An auditory oddball (P300) spelling system for brain‐computer interfaces – https://onlinelibrary.wiley.com/doi/10.1111/j.1469-8986.2008.00783.x

[128] Markovinović, et al. – Design considerations for the auditory brain computer interface speller – https://www.sciencedirect.com/science/article/abs/pii/S1746809422000684?via%3Dihub

[129] Augustine, et al. – Neuroscience. 2nd edition – https://www.ncbi.nlm.nih.gov/books/NBK10900/

[130] Brewer & Barton – Maps of the Auditory Cortex – https://pmc.ncbi.nlm.nih.gov/articles/PMC6436392/

[131] ScienceDirect – Auditory Cortex – https://www.sciencedirect.com/topics/psychology/auditory-cortex

[132] Wang, et al. – Conformal in-ear bioelectronics for visual and auditory brain-computer interfaces – https://www.nature.com/articles/s41467-023-39814-6

[133] Deuel, et al. – Musical auditory feedback BCI: clinical pilot study of the Encephalophone – https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2025.1592640/full

[134] Pontevedra, M. – Function and Association of Auditory Cortex with Brain – https://www.longdom.org/open-access/function-and-association-of-auditory-cortex-with-brain-99342.html

[135] Choi, et al. – Design of auditory P300-based brain-computer interfaces with a single auditory channel and no visual support – https://link.springer.com/article/10.1007/s11571-022-09901-3

[136] McCreadie, et al. – Sensorimotor learning with stereo auditory feedback for a brain–computer interface – https://link.springer.com/article/10.1007/s11517-012-0992-7

[137] Nijboer, et al. – The influence of psychological state and motivation on brain–computer interface performance in patients with amyotrophic lateral sclerosis – a longitudinal study – https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2010.00055/full

[138] Nijboer, et al. – An auditory brain-computer interface (BCI) – ​​https://www.sciencedirect.com/science/article/abs/pii/S0165027007000726?via%3Dihub

[139] Kiss & Linnell – The effect of preferred background music on task-focus in sustained attention – https://link.springer.com/article/10.1007/s00426-020-01400-6

[140] John Hopkins Medicine – Brain Anatomy and How the Brain Works – https://www.hopkinsmedicine.org/health/conditions-and-diseases/anatomy-of-the-brain

[141] NIH – Brain Basics: Know Your Brain – https://www.ninds.nih.gov/health-information/public-education/brain-basics/brain-basics-know-your-brain

[142] Messe, et al. – Relating Structure and Function in the Human Brain: Relative Contributions of Anatomy, Stationary Dynamics, and Non-stationarities – https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003530

[143] Sporns, O. – The human connectome: Origins and challenges – https://www.sciencedirect.com/science/article/abs/pii/S1053811913002656

[144] Honey, et. al – Can structure predict function in the human brain? – https://www.sciencedirect.com/science/article/abs/pii/S1053811910000935

[145] Damoiseaux & Greicius – Greater than the sum of its parts: a review of studies combining structural connectivity and resting-state functional connectivity – https://link.springer.com/article/10.1007/s00429-009-0208-6

[146] Bullmore & Sporns – Complex brain networks: graph theoretical analysis of structural and functional systems – https://www.nature.com/articles/nrn2575

[147] Ackerman, S. – Discovering the Brain: Major Structures and Functions of the Brain – https://www.ncbi.nlm.nih.gov/books/NBK234157/

[148] Amunts, et al. – Linking Brain Structure, Activity, and Cognitive Function through Computation – https://www.eneuro.org/content/9/2/ENEURO.0316-21.2022

[149] Liu, et al. – Brain Structure and Function Show Distinct Relations With Genetic Predispositions to Mental Health and Cognition – https://www.sciencedirect.com/science/article/pii/S2451902222001860

[150] Grasby, et al. – The genetic architecture of the human cerebral cortex – https://www.science.org/doi/abs/10.1126/science.aay6690

[151] ScienceDirect – Cerebral Cortex: Function and dysfunction of the cerebral lobes – https://www.sciencedirect.com/topics/neuroscience/cerebral-cortex