Analysis and application of functional connectivity in synchronic hybrid mental tasks for brain-computer interface

This paper presents the classification of three mental tasks, using the EEG signal and simulating a real-time process, what is known as pseudo-online technique. The Bayesian classifier is used to recognize the mental tasks, the feature extraction uses the Power Spectral Density, and the Sammon map is used to visualize the class separation. The choice of the EEG channel and sampling frequency is based on the Kullback-Leibler symmetric divergence and a reclassification model is proposed to stabilize the classifications.

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A Synchronized Hybrid Brain-Computer Interface System for Simultaneous Detection and Classification of Fusion EEG Signals

Complexity ◽

10.1155/2020/4137283 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Dalin Yang ◽

Trung-Hau Nguyen ◽

Wan-Young Chung

Keyword(s):

Technology Use ◽

Simple Structure ◽

Brain Computer Interface ◽

Simultaneous Detection ◽

High Accuracy ◽

Computer Interface ◽

Eeg Signals ◽

Fast Independent Component Analysis ◽

Bci System ◽

Mental Tasks

Brain-computer interface (BCI) technology represents a fast-growing field of research and applications for disabled and healthy people, which is a direct communication pathway to translate the neural information into an active command. Owing to the complicated headset structure, low accuracies, extended training periods, and nonstationary noises, BCI still has many challenges that should be dealt with for further facilitation of BCI technology use in daily life. In this study, a simplified synchronized hybrid BCI system is proposed for multiple command control by the electroencephalograph (EEG) signals in the motor cortex. This system can detect the single motor imagery (MI) task, single steady-state visually evoked potential (SSVEP) task, and hybrid MI + SSVEP tasks simultaneously (total ten mental tasks) via 2 EEG channels with high accuracy. The fast independent component analysis algorithm is employed to hybrid signals for obtaining clear EEG signals resulting from denoising. Feature extraction is performed by the wavelet transform, which is extracted by the features in the frequency and time domains. Furthermore, a four-layer convolutional neural network (CNN) is used as a classifier to distinguish different mental tasks. Finally, the hybrid MI + SSVEP system with a simple structure achieves a high accuracy of 95.56%. Additionally, the single MI-based and the SSVEP-based BCI system obtain the classification accuracy of 90.16% and 93.21%, respectively. Experimental results indicate that the synchronized hybrid BCI system could achieve multiple command control with a simple structure. In comparison with the single MI-based and the SSVEP-based BCI system, the hybrid MI + SSVEP BCI system shows a stable performance and higher efficiency. The proposed investigation provides a new method for the multiple command control by a hybrid BCI system. Also, the proposed BCI system offers the possibility of friendly utilization for disabled people because of its reliability, ease of use, and simplified headset structure.

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A Novel Design of 4-Class BCI Using Two Binary Classifiers and Parallel Mental Tasks

Computational Intelligence and Neuroscience ◽

10.1155/2008/437306 ◽

2008 ◽

Vol 2008 ◽

pp. 1-5 ◽

Cited By ~ 15

Author(s):

Tao Geng ◽

John Q. Gan ◽

Matthew Dyson ◽

Chun SL Tsui ◽

Francisco Sepulveda

Keyword(s):

Brain Computer Interface ◽

Computer Interface ◽

Classification Algorithms ◽

Single Trial ◽

Linear Discriminant ◽

The Subject ◽

Offline Analysis ◽

Binary Classifiers ◽

Novel Design ◽

Mental Tasks

A novel 4-class single-trial brain computer interface (BCI) based on two (rather than four or more) binary linear discriminant analysis (LDA) classifiers is proposed, which is called a “parallel BCI.” Unlike other BCIs where mental tasks are executed and classified in a serial way one after another, the parallel BCI uses properly designed parallel mental tasks that are executed on both sides of the subject body simultaneously, which is the main novelty of the BCI paradigm used in our experiments. Each of the two binary classifiers only classifies the mental tasks executed on one side of the subject body, and the results of the two binary classifiers are combined to give the result of the 4-class BCI. Data was recorded in experiments with both real movement and motor imagery in 3 able-bodied subjects. Artifacts were not detected or removed. Offline analysis has shown that, in some subjects, the parallel BCI can generate a higher accuracy than a conventional 4-class BCI, although both of them have used the same feature selection and classification algorithms.

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Brain computer interface control via functional connectivity dynamics

Pattern Recognition ◽

10.1016/j.patcog.2011.04.034 ◽

2012 ◽

Vol 45 (6) ◽

pp. 2123-2136 ◽

Cited By ~ 49

Author(s):

Ian Daly ◽

Slawomir J. Nasuto ◽

Kevin Warwick

Keyword(s):

Functional Connectivity ◽

Brain Computer Interface ◽

Computer Interface ◽

Interface Control

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Phase Synchronization for the Recognition of Mental Tasks in a Brain–Computer Interface

IEEE Transactions on Neural Systems and Rehabilitation Engineering ◽

10.1109/tnsre.2004.838443 ◽

2004 ◽

Vol 12 (4) ◽

pp. 406-415 ◽

Cited By ~ 96

Author(s):

E. Gysels ◽

P. Celka

Keyword(s):

Phase Synchronization ◽

Brain Computer Interface ◽

Computer Interface ◽

Mental Tasks

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Performance Prediction for a Near-Infrared Spectroscopy-Brain–Computer Interface Using Resting-State Functional Connectivity of the Prefrontal Cortex

International Journal of Neural Systems ◽

10.1142/s0129065718500235 ◽

2018 ◽

Vol 28 (10) ◽

pp. 1850023 ◽

Cited By ~ 10

Author(s):

Jaeyoung Shin ◽

Chang-Hwan Im

Keyword(s):

Prefrontal Cortex ◽

Infrared Spectroscopy ◽

Functional Connectivity ◽

Near Infrared Spectroscopy ◽

Resting State ◽

Near Infrared ◽

Mental Arithmetic ◽

Brain Computer Interface ◽

Computer Interface ◽

Resting State Functional Connectivity

One of the most important issues in current brain–computer interface (BCI) research is the prediction of a user’s BCI performance prior to the main BCI session because it would be useful to reduce the time required to determine the BCI paradigm best suited to that user. In electroencephalography (EEG)-BCI research, whether a user has low BCI performance toward a specific BCI paradigm has been estimated using a variety of resting-state EEG features. However, no previous study has attempted to predict the performance of near-infrared spectroscopy (NIRS)-BCI using resting-state NIRS data recorded before the main BCI experiment. In this study, we investigated whether the performance of an NIRS-BCI discriminating a mental arithmetic task from the baseline state could be predicted using resting-state functional connectivity (RSFC) of the prefrontal cortex. The investigation of NIRS signals recorded from 29 participants revealed that the RSFC between bilateral channels in the prefrontal area was negatively correlated with subsequent BCI performance (e.g. a fitted line for the RSFC between L2 and R2 channels explains 41% of BCI performance variation). We expect that our indicator can be used to predict BCI performance of an individual user prior to the main NIRS-BCI experiments, thereby facilitating implementation of more efficient NIRS-BCI systems.

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