2020 International brain–computer interface competition: A review

Abstract

The brain-computer interface (BCI) has been investigated as a form of communication tool between the brain and external devices. BCIs have been extended beyond communication and control over the years. The 2020 international BCI competition aimed to provide high-quality neuroscientific data for open access that could be used to evaluate the current degree of technical advances in BCI. Although there are a variety of remaining challenges for future BCI advances, we discuss some of more recent application directions: (i) few-shot EEG learning, (ii) micro-sleep detection (iii) imagined speech decoding, (iv) cross-session classification, and (v) EEG(+ear-EEG) detection in an ambulatory environment. Not only did scientists from the BCI field compete, but scholars with a broad variety of backgrounds and nationalities participated in the competition to address these challenges. Each dataset was prepared and separated into three data that were released to the competitors in the form of training and validation sets followed by a test set. Remarkable BCI advances were identified through the 2020 competition and indicated some trends of interest to BCI researchers.

Identifying Thematics in a Brain‐Computer Interface Research

“…The center of the word-association frequency diagram illustrated the away from “BCI” and its composite words to “control,” “performance” and “state,” highlighting the shift in the review literature focus away from how BCI system operates and the move to the practical real-world application [ 4 , 26 , 27 , 29 , 35 , 37 , 90 ] ( Figure 6 ). There is a district clustering in the 2018 onward diagram that is indicative of the focus center of research and is centered on “human,” “user,” and terms such as “cognitive” “process,” “technology,” and “response,” terms associated with the application of BCI [ 29 , 40 , 44 , 97 ] ( Figure 6 ).…”

“…Te center of the word-association frequency diagram illustrated the away from "BCI" and its composite words to "control," "performance" and "state," highlighting the shift in the review literature focus away from how BCI system operates and the move to the practical real-world application [4,26,27,29,35,37,90] (Figure 6). Tere is a district clustering in the 2018 onward diagram that is indicative of the focus center of research and is centered on "human," "user," and terms such as "cognitive" "process," "technology," and "response," terms associated with the application of BCI [29,40,44,97] (Figure 6). Early reviews focused on the methods and achieved greater performance with words such as "access pathways" [66], "alternative communication" [67], "biocompatibility" [43], "classifcation" [57], "cognitive tasks" [31], "control" [33], "motor imagery" [7,36,70,79], "neuroengineering" [19,43] "neuroimaging" [37], and "sensory-motor regions" [79], all of which show a focus on improving the understanding of brain function and how BCI systems are able to exploit this knowledge.…”

“…Tere has been a shift in the research paradigms in the BCI feld and this review seeks to understand those shifts in terms of thematic changes in the literature [24][25][26][27][28][29]. At present, there is little understanding of how the themes in the literature have changed through time.…”

An Adaptive Task-Related Component Analysis Method for SSVEP Recognition

“…Hence, one of the used SSVEP datasets has been recorded using an EPOC EEG mobile device. Furthermore, a short calibration of a BCI system, described as few-shot EEG learning [ 37 ], which uses minimal training data, is a major challenge in the BCI community. Hence, in our study, we provide experiments with respect to this issue.…”

Real-Time Deep Neurolinguistic Learning Enhances Noninvasive Neural Language Decoding for Brain–Machine Interaction

“…A major challenge for this interaction is to naturally ensemble the human mind and machines, decode intuitive human intentions, achieve high accuracy, and communicate in real time [11], [12], [13]. Recently, speech-imagery decoding from brain signals has been investigated as a BMI paradigm.…”