Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review

By M. Rashid, Norizam bin Sulaiman, Anwar P. P. Abdul Majeed, Rabiu Muazu Musa, Ahmad Fakhri Ab. Nasir, Bifta Sama Bari, Sabira Khatun. Frontiers in Neurorobotics, 2020

427 citations recorded by the source corpus.

Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices through the utilization of brain waves. It is worth noting that the application of BCI is not limited to medical applications, and hence, the research in this field has gained due attention. Moreover, the significant number of related publications over the past two decades further indicates the consistent improvements and breakthroughs that have been made in this particular field. Nonetheless, it is also worth mentioning that with these improvements, new challenges are constantly discovered. This article provides a comprehensive review of the state-of-the-art of a complete BCI system. First, a brief overview of electroencephalogram (EEG)-based BCI systems is given. Secondly, a considerable number of popular BCI applications are reviewed in terms of electrophysiological control signals, feature extraction, classification algorithms, and performance evaluation metrics. Finally, the challenges to the recent BCI systems are discussed, and possible solutions to mitigate the issues are recommended.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/ed57cb2e56c4aef06279f2edf2525ab05ccb10d7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

1. I. Chavdarov
2. Ganesh R. Naik
3. A. Lekova

In Cognitive Neuroscience

1. I. Chavdarov
2. Ganesh R. Naik
3. A. Hammad
4. Essam H. Houssein
5. A. Lekova
6. Abdelmgeid A. Ali

Citations in the corpus

• Human emotion recognition from EEG-based brain–computer interface using machine learning: a comprehensive review (2022, background): “Spelling systems, medical neuroergonomics, wheelchair control, virtual reality, robot control, mental workload monitoring, gaming, driver fatigue monitoring, environment management, biometrics systems, and emotion detection are among the most significant successes in EEG-based BCIs [7].”
• Progress in Brain Computer Interface: Challenges and Opportunities (2019, methodology): “For specialized reviews on particular BCI topics we refer to the recent literature (McFarland et al., 2006; Schwartz et al., 2006; Bashashati et al., 2007; Lotte et al., 2007, 2018; Matthews et al., 2007; Sitaram et al., 2007; Mak and Wolpaw, 2009; Menon et al., 2009; Nicolelis and Lebedev, 2009; Su”
• Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review (2021, methodology): “When a continuous parameter is used for classification, the area under the curve (AUC) [100, 107] and receiver operating characteristic (ROC) [63, 118] are often used [29].”
• A Fuzzy Shell for Developing an Interpretable BCI Based on the Spatiotemporal Dynamics of the Evoked Oscillations (2021, background): “More information for deep learning and unsupervised and semisupervised learning algorithms could be found in [12, 14].”