EEG-Based Brain-Computer Interfaces: A Novel Neurotechnology and Computational Intelligence Method

Abstract

Abstract-Significant advances in neuroscience, sensor technologies, and efficient signal processing algorithms have greatly facilitated the transition from laboratory-oriented neuroscience research to practical applications. Brain-computer interfaces (BCIs) represent major strides in translating brain signals into actionable decisions and primarily consist of hardware and software that guide the communications between users and systems. This article presents several current neurotechnologies and computational intelligence methods applied to EEG-based BCIs. In the hardware aspect, novel portable EEG devices featuring dry electrodes are introduced as substitutes for traditional BCIs with wet electrodes and its bulky size. With these advantages, these novel EEG devices can acquire real-time EEG signals for operational workplaces without requiring conductive gel/paste or scalp preparations. As for the software aspect, blind source separation, artificial neural networks, effective connectivity measurements and information fusion techniques are introduced to address the technical issues of artifact removal, rapid event-related potential detection, complex brain network description, and decision fusion, respectively. For instance, information fusion technique has been utilized to attack the individual differences problem of motor imagery applications in the real-world environment. With continuous improvements in the development of a convenient approach to record brain signals and extract knowledge regarding intentions, BCI techniques are envisioned to lead to a wide range of real-life applications in the near future.

A Wireless Multifunctional SSVEP-Based Brain–Computer Interface Assistive System

“…O1 and O2 channels of Mindo 4S with spring-loaded dry sensors were adopted for the EEG acquisition in the SSVEP experiment and placed according to the international 10-20 system. The Mindo 4S, sampling at rates of 250 Hz, was designed and developed by the Brain Research Center, National Chiao Tung University [20]. No conductive gel or skin preparation was necessary.…”

“…This four-channel EEG cap, Mindo 4S, was developed by the Brain Research Center of National Chiao Tung University in Hsinchu, Taiwan. Equipped with spring-loaded sensors which enable electrodes to contact the scalp through the hair, Mindo 4S can record usable EEG data with good quality after simply putting it on [20]. Table 2 shows the Mindo 4S specifications.…”

How Physical Activities Affect Mental Fatigue Based on EEG Energy, Connectivity, and Complexity

“…Another study of French drivers showed that 46% of the drivers experiencing near-misses reported related sleepiness and 5.2% of the car accidents were caused by sleepiness ( 8 ). Fatigue can affect the drivers' performance regarding EEG and functional near-infrared spectroscopy (fNIRS) ( 9 , 10 ). Mental fatigue is also a contributing factor for some medical conditions, such as cardiovascular diseases ( 11 ), hypothyroidism ( 12 ), and fibromyalgia ( 13 ).…”

Multi-channel EEG recordings during a sustained-attention driving task

“…In 2003, we began conducting laboratorybased experiments collecting EEG data to investigate brain function associated with sustained attention on a safe driving task 5,6 . Our experiments have two distinct two goals: 1) neurocognitive performance: designing key signatures of how the neurocognitive state of the driver (e.g., physical and physiological) varies when faced with the sensory, perceptual and cognitive demands of a sustained-attention situation [7][8][9] ; and 2) advanced computational approaches: investigating novel computational, statistical modelling and data visualisation techniques to extract signatures of neurocognitive performance, including novel analytic and algorithmic approaches for individually assessing drivers' neurocognitive state and performance 10,11 .…”