28 Jan 2020
” Brain-Computer Interface (BCI) is a powerful communication tool between users and systems, which enhances the capability of the human brain in communicating and interacting with the environment directly. Advances in neuroscience and computer science in the past decades have led to exciting developments in BCI, thereby making BCI a top interdisciplinary research area in computational neuroscience and intelligence. Recent technological advances such as wearable sensing devices, real-time data streaming, machine learning, and deep learning approaches have increased interest in electroencephalographic (EEG) based BCI for translational and healthcare applications. Many people benefit from EEG-based BCIs, which facilitate continuous monitoring of fluctuations in cognitive states under monotonous tasks in the workplace or at home. In this study, we survey the recent literature of EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensated for the gaps in the systematic summary of the past five years (2015-2019). In specific, we first review the current status of BCI and its significant obstacles. Then, we present advanced signal sensing and enhancement technologies to collect and clean EEG signals, respectively. Furthermore, we demonstrate state-of-art computational intelligence techniques, including interpretable fuzzy models, transfer learning, deep learning, and combinations, to monitor, maintain, or track human cognitive states and operating performance in prevalent applications. Finally, we deliver a couple of innovative BCI-inspired healthcare applications and discuss some future research directions in EEG-based BCIs “.
DISCUSSION AND CONCLUSION
“In this paper, we systematically survey the recent advances in advances of the dry sensor, wearable devices, signal enhancement, transfer learning, deep learning, and interpretable fuzzy models for EEG-based BCIs. The various computational intelligence approaches enable us to learn reliable brain cortex features and understand human knowledge from EEG signals. In a word, we summarise the recent EEG signal sensing and interpretable fuzzy models, followed by discussing dominant transfer and deep learning for BCI applications. Finally, we overview healthcare applications and point out the open challenges and future directions”.
References & to read the full text:
- arXiv:2001.11337v1 [eess.SP] for this version
- arXiv: https://arxiv.org/abs/2001.11337
- PDF: https://arxiv.org/pdf/2001.11337.pdf
Xiaotong Gu, Zehong Cao*, Member, IEEE, Alireza Jolfaei, Member, IEEE, Peng Xu, Member, IEEE, Dongrui Wu, Senior Member, IEEE, Tzyy-Ping Jung, Fellow, IEEE, and Chin-Teng Lin, Fellow, IEEE