EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies
on Signal Sensing Technologies and Computational Intelligence Approaches and
their Applications
- URL: http://arxiv.org/abs/2001.11337v1
- Date: Tue, 28 Jan 2020 10:36:26 GMT
- Title: EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies
on Signal Sensing Technologies and Computational Intelligence Approaches and
their Applications
- Authors: Xiaotong Gu, Zehong Cao, Alireza Jolfaei, Peng Xu, Dongrui Wu,
Tzyy-Ping Jung, Chin-Teng Lin
- Abstract summary: Brain-Computer Interface (BCI) is a powerful communication tool between users and systems.
Recent technological advances have increased interest in electroencephalographic (EEG) based BCI for translational and healthcare applications.
- Score: 65.32004302942218
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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