Functional Mapping of the Brain for Brain–Computer Interfacing: A Review

Abstract

Brain–computer interfacing has been applied in a range of domains including rehabilitation, neuro-prosthetics, and neurofeedback. Neuroimaging techniques provide insight into the structural and functional aspects of the brain. There is a need to identify, map and understand the various structural areas of the brain together with their functionally active roles for the accurate and efficient design of a brain–computer interface. In this review, the functionally active areas of the brain are reviewed by analyzing the research available in the literature on brain–computer interfacing in conjunction with neuroimaging experiments. This review first provides an overview of various approaches of brain–computer interfacing and basic components in the BCI system and then discuss active functional areas of the brain being utilized in non-invasive brain–computer interfacing performed with hemodynamic signals and electrophysiological recording-based signals. This paper also discusses various challenges and limitations in BCI becoming accessible to a novice user, including security issues in the BCI system, effective ways to overcome those issues, and design implementations.

State-of-the-Art on Brain-Computer Interface Technology

“…collected from EEG recordings can also be analyzed to assess cognitive abilities such as attention span or memory recall speed. Figure 3 illustrates that there are four distinct "rhythms" of the human brain, which can be categorized based on their frequency: δ delta (0.1-4 Hz), θ theta (4-7.5 Hz), α alpha (7.5-12 Hz), β beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and γ gamma (over 30 Hz). It is important to note that these rhythms differ in amplitude as well as frequency.…”

“…Magnetoencephalography is a non-invasive brain imaging technique that measures magnetic fields generated by electrical currents inside neurons [12,35,36,62,63]. It provides high temporal resolution with excellent spatial accuracy and can be used to track changes in neural activity related to cognitive processes such as attention or memory formation over time.…”

“…The use of BCIs in neuroprosthetics is a rapidly growing field, with potential applications ranging from restoring communication to those who have lost it due to injury or illness to providing enhanced control of prosthetic limbs. BCI technology has been used for decades in the medical sector but only recently began being applied to the development of neuroprostheses [9,12,119,120].…”

Bio-mimetic sensing characteristics of differently synthesized polyanilines towards the surrounding electrical and chemical energetic conditions

“…The current scientific community faces a challenging task in emulating the sensory capabilities of natural muscles, which have the remarkable ability to perceive their environment and relay this information to the brain as electrical signals via sensory neurons [1,2]. They also possess the capacity to produce actuation while sensing, owing to their reactive gel nature that expands or contracts in response to electrical signals from the brain [3,4].…”

7T Magnetic Compatible Multimodality Electrophysiological Signal Recording System

“…The main method of AAS to remove the gradient artifact is to construct the template of the gradient artifact and then subtract the artifact template from the original signal to obtain a clean EEG signal. In recent years, more and more machine learning and deep learning algorithms have been applied to EEG processing, as well as more and more research towards the automatic removal of gradient artifacts [21][22][23][24]. Duffy et al first used denoising autoencoders (DAE) to remove gradient artifacts for simultaneous EEG-fMRI automatically [25].…”