EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications

Abstract

Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact with the environment. Recent advancements in technology and machine learning algorithms have increased interest in electroencephalographic (EEG)-based BCI applications. EEG-based intelligent BCI systems can facilitate continuous monitoring of fluctuations in human cognitive states under monotonous tasks, which is both beneficial for people in need of healthcare support and general researchers in different domain areas. In this review, we survey the recent literature on EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensating for the gaps in the systematic summary of the past five years. Specifically, we first review the current status of BCI and signal sensing technologies for collecting reliable EEG signals. Then, we demonstrate state-of-the-art computational intelligence techniques, including fuzzy models and transfer learning in machine learning and deep learning algorithms, to detect, monitor, and maintain human cognitive states and task performance in prevalent applications. Finally, we present a couple of innovative BCI-inspired healthcare applications and discuss future research directions in EEG-based BCI research.

Investigating the Effect of COVID-19 Infection on P300 Based BCI Application Performance

“…Based on the rough evaluation of Figure 9 and Figure 10, it is observed that the obtained CA is about 95-100% especially for group as well as occipital region electrodes with 15 flashings. When compared with similar results of literature, these results are consistent, and the current study is considered to perform well [28]. To further investigate, the before and after COVID-19 infection performances, namely red and blue radar plots in Figure 9 and 10, are compared by each other.…”

An overlapping sliding window and combined features based emotion recognition system for EEG signals

“…Despite significant development in the field of DL and its suitability to various applications, almost 59% of researchers have used an SVM with RBF kernels for BCIs [19]. This is due to the unavailability of a large-scale data set for BCIs.…”

“…After feature extraction, the machine learning (ML) and DL methods are primarily applied in literature for classification [19]. The ML methods applied are k-nearest neighbour (KNN), random forest (RF), decision tree (DT), neural network (NN) and support vector machine (SVM) for ER.…”

Brain-Computer Interface: Advancement and Challenges

“…[13] This paper refers to applying only noninvasive techniques on BCI and profound learning-related BCI studies This study exclusively covers noninvasive brain signals. [14] This review focused on popular techniques such as deep learning models and advances in signal sensing technologies Popular feature extraction processes, methods, and classifiers are not mentioned or reviewed.…”

“…The prominent deep learning techniques and cutting-edge models for brain signals are presented in this paper, together with specific ideas for selecting the best deep learning models. Gu, X. et al [14] investigated the most current research on EEG signal detection technologies and computational intelligence methodologies in BCI systems that filled in the loopholes in the five-year systematic review (2015)(2016)(2017)(2018)(2019). The authors demonstrated sophisticated signal detecting and augmentation technologies for collecting and cleaning EEG signals.…”