Hybrid Brain–Computer Interface Spellers: A Walkthrough Recent Advances in Signal Processing Methods and Challenges

Abstract

Hybrid brain computer interfaces (hBCIs) have emerged as a possible path to integrated brain-computer interaction in current history. hBCI, is a device formed by the amalgamation of control signal and bio-signal combination of control signal with one or more bio-signals that enhances system performance and usability. Assistive technology, spelling, and gaming are some of the primary areas where hBCI can be used. Among these, hBCI spellers are the popular application that has opened up a slew of new possibilities of communication for paralyzed individuals. The goal of this review is to provide prospect on current state of hBCI spellers covering types of hBCI, signal processing methods and evaluation with emphasis on feature extraction techniques and classification methods used by these studies. The authors anticipate that this analysis will serve as a foundation for ongoing studies on hBCI spellers.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Nupur Chugh

Nupur Chugh is currently pursuing Ph.D. at NSUT, India. She is a passionate researcher and is intrigued about the application of Artificial intelligence and Machine Learning in the domain of Brain-computer Interface and aspects of explainability. She has published several research papers in these domains.

Swati Aggarwal

Swati Aggarwal is working as an Assistant Professor at Computer Science Department, NSUT, India. Her active research interests lie at the intersection of Artificial Intelligence, Deep Learning, Brain-computer Interface (BCI) and Cognitive Computation. She is currently pursuing prestigious MSCA-IF post-doctoral research on developing BCI for evaluating perceptual development in typically-developing infants.