Mind the gap: State-of-the-art technologies and applications for EEG-based brain–computer interfaces

Abstract

Brain–computer interfaces (BCIs) provide bidirectional communication between the brain and output devices that translate user intent into function. Among the different brain imaging techniques used to operate BCIs, electroencephalography (EEG) constitutes the preferred method of choice, owing to its relative low cost, ease of use, high temporal resolution, and noninvasiveness. In recent years, significant progress in wearable technologies and computational intelligence has greatly enhanced the performance and capabilities of EEG-based BCIs (eBCIs) and propelled their migration out of the laboratory and into real-world environments. This rapid translation constitutes a paradigm shift in human–machine interaction that will deeply transform different industries in the near future, including healthcare and wellbeing, entertainment, security, education, and marketing. In this contribution, the state-of-the-art in wearable biosensing is reviewed, focusing on the development of novel electrode interfaces for long term and noninvasive EEG monitoring. Commercially available EEG platforms are surveyed, and a comparative analysis is presented based on the benefits and limitations they provide for eBCI development. Emerging applications in neuroscientific research and future trends related to the widespread implementation of eBCIs for medical and nonmedical uses are discussed. Finally, a commentary on the ethical, social, and legal concerns associated with this increasingly ubiquitous technology is provided, as well as general recommendations to address key issues related to mainstream consumer adoption.

Towards Cognitive Authentication for Smart Healthcare Applications

“…In addition, this procedure forms a unique matrix by well-defined and clear steps. Most popular example of a chaotic process is the chebyshev chaotic filters [ 50 , 51 , 52 , 53 ] for further data generation operation.…”

A Graph Fourier Transform Based Bidirectional Long Short-Term Memory Neural Network for Electrophysiological Source Imaging

“…EEG/MEG source imaging (ESI), also known as EEG/MEG source localization, is a non-invasive neuroimaging technology that infers the location, direction, and distribution of the corresponding brain sources from the EEG or MEG data ( He et al, 2018 ). Compared with the invasive modalities, the recording of EEG/MEG signals imposes minimum risks of blooding and inflammation of the brain ( Portillo-Lara et al, 2021 ). Compared to other non-invasive brain imaging modalities, like computed tomography (CT), positron emission tomography (PET), functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS), the temporal resolution of EEG is up to a millisecond ( He et al, 2018 ), which allows it to track the electrical activity of neurons in smaller temporal granularity ( Numata et al, 2019 ).…”

A State-of-the-Art Review of EEG-Based Imagined Speech Decoding

“…Different types of BCI systems have been proposed by researchers to be able to use them in real-life scenarios. Some of the most important BCI classifications are: synchronous vs. asynchronous, online vs. offline, exogenous vs. endogenous, invasive vs. non-invasive (Portillo-Lara et al, 2021 ).…”