Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review
Mamunur Rashid,Norizam Sulaiman,Anwar P. P. Abdul Majeed,Rabiu Muazu Musa,Ahmad Fakhri Ab. Nasir,Bifta Sama Bari,Sabira Khatun +6 more
TL;DR: This article provides a comprehensive review of the state-of-the-art of a complete BCI system and a considerable number of popular BCI applications are reviewed in terms of electrophysiological control signals, feature extraction, classification algorithms, and performance evaluation metrics.
read more
Abstract: 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.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review
Hamdi Altaheri,Ghulam Muhammad,Mansour Alsulaiman,Syed Umar Amin,Ghadir Ali Altuwaijri,Ghadir Ali Altuwaijri,Wadood Abdul,Mohamed A. Bencherif,Mohammed Faisal +8 more
TL;DR: This work systematically review the DL-based research for MI-EEG classification from the past ten years, summarizes MI- EEG-based applications, extensively explores public MI-eeG datasets, and gives an overall visualization of the performance attained for each dataset based on the reviewed articles.
334
EEG-Based BCI Emotion Recognition: A Survey.
TL;DR: A survey of the pertinent scientific literature from 2015 to 2020 presents trends and a comparative analysis of algorithm applications in new implementations from a computer science perspective and provides insights for future developments.
236
Human emotion recognition from EEG-based brain–computer interface using machine learning: a comprehensive review
TL;DR: In this article , a review of the EEG-based emotion recognition methods is presented, including feature extraction, feature selection/reduction, machine learning methods (e.g., k-nearest neighbor), support vector machine, decision tree, artificial neural network, random forest, and naive Bayes) and deep learning methods.
Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals
Zied Tayeb,Juri Fedjaev,Nejla Ghaboosi,Christoph Richter,Lukas Everding,Xingwei Qu,Yingyu Wu,Gordon Cheng,Jörg Conradt +8 more
TL;DR: Better classification performance was achieved with deep learning models compared to state-of-the art machine learning techniques, which could chart a route ahead for developing new robust techniques for EEG signal decoding.
198
A Review of EEG Signal Features and Their Application in Driver Drowsiness Detection Systems
TL;DR: In this article, the authors reviewed the applications of EEG features and deep learning approaches in driver drowsiness detection, and discussed the open challenges and opportunities in improving driver Drowsiness Detection based on EEG.
164
References
To What Extent can Dry and Water-based EEG Electrodes Replace Conductive Gel Ones? - A Steady State Visual Evoked Potential Brain-computer Interface Case Study.
Vojkan Mihajlovic,Gary Nelson Garcia Molina,Jan Peuscher +2 more
- 01 Jan 2012
TL;DR: These results demonstrate that, having optimized headset and electrode design for dry and water-based electrodes for people with different hair length and type, dry andWater-based electrode scan replace gel ones in BCIs and Neurofeedback applications where lower communication speed is acceptable.
15
The Detection of P300 Potential Based on Deep Belief Network
Zhaohua Lu,Ning Gao,Yuanzi Liu,Qi Li +3 more
- 01 Oct 2018
TL;DR: This paper proposed a P300 potential detection method based on deep belief network (DBN) which can extract useful feature information from raw data without data preprocessing and showed that the DBN has a good feature learning ability for the P 300 potential and has achieved the better detection results.
15
Feature extraction of P300s in EEG signal with discrete wavelet transform and fisher criterion
Guo Shunying,Lin Suyun,Huang Zhihua +2 more
- 01 Oct 2015
TL;DR: A feature extraction method that combines discrete wavelet transform (DWT) with Fisher criterion for P300 detection and is better than the existing method used in BCI2000, in terms of averaged accuracy over 238 runs.
15
EEG-controlled Wheelchair for ALS Patients
A. Kodi,D. Kumar,D. Kodali,I. A. Pasha +3 more
- 06 Apr 2013
TL;DR: An EEG-based BCI is built to help ALS patients with lack of focused attention perform their everyday activities in a less dependent manner and to enable them to move their wheelchair in the direction they want or to guide them to their destination.
15
Deceit Identification Test on EEG Data Using Deep Belief Network
Annushree Bablani,Damodar Reddy Edla,Venkatanareshbabu Kuppili +2 more
- 01 Jul 2018
TL;DR: A deep learning technique using the restricted Boltzmann machine with wavelet to obtain the time and frequency domain information of signals and a deep belief network is developed with four RBMs stacked together to classify EEG data into guilty and innocent.
14