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Dear Colleagues,

Over the past few decades, brain–computer interface (BCI) systems have been significantly improved, and thus, have great potential as a future communication tool. Traditionally, various machine learning algorithms have been used to extract significant features for decoding brain states or humans’ direct or indirect intentions. Recently, researchers have been applying deep learning models to BCI systems, leading to improved performance in the fields of computer vision, natural language processing, etc. However, there are some issues in the BCI field that may come from non-stationarity or low signal-to-noise ratio of signals recorded from the brain. Therefore, sophisticated approaches based on novel machine or deep learning models are needed to advance the research field by overcoming these issues. The ultimate goal of this Special Issue is to share ideas, approaches, opinions, and comments from researchers in the BCI field, as well as other related research fields.

Prof. Dr. Sangtae Ahn
Guest Editor

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Keywords

brain–computer interface (BCI)
intelligent brain signal processing
deep learning and machine learning
continual learning, lifelong learning, transfer learning
data augmentation
explainable artificial intelligence
diagnosis and prediction of diseases
practical brain–computer interface
advances and challenges on brain–computer interface

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Research

42 pages, 13327 KiB

Open AccessArticle

Dimension Reduction Using New Bond Graph Algorithm and Deep Learning Pooling on EEG Signals for BCI

by Ahmad Naebi, Zuren Feng, Farhoud Hosseinpour and Gahder Abdollahi

Appl. Sci. 2021, 11(18), 8761; https://doi.org/10.3390/app11188761 - 20 Sep 2021

Cited by 3 | Viewed by 2275

Abstract

One of the main challenges in studying brain signals is the large size of the data due to the use of many electrodes and the time-consuming sampling. Choosing the right dimensional reduction method can lead to a reduction in the data processing time. Evolutionary algorithms are one of the methods used to reduce the dimensions in the field of EEG brain signals, which have shown better performance than other common methods. In this article, (1) a new Bond Graph algorithm (BGA) is introduced that has demonstrated better performance on eight benchmark functions compared to genetic algorithm and particle swarm optimization. Our algorithm has fast convergence and does not get stuck in local optimums. (2) Reductions of features, electrodes, and the frequency range have been evaluated simultaneously for brain signals (left-handed and right-handed). BGA and other algorithms are used to reduce features. (3) Feature extraction and feature selection (with algorithms) for time domain, frequency domain, wavelet coefficients, and autoregression have been studied as well as electrode reduction and frequency interval reduction. (4) First, the features/properties (algorithms) are reduced, the electrodes are reduced, and the frequency range is reduced, which is followed by the construction of new signals based on the proposed formulas. Then, a Common Spatial Pattern is used to remove noise and feature extraction and is classified by a classifier. (5) A separate study with a deep sampling method has been implemented as feature selection in several layers with functions and different window sizes. This part is also associated with reducing the feature and reducing the frequency range. All items expressed in data set IIa from BCI competition IV (the left hand and right hand) have been evaluated between one and three channels, with better results for similar cases (in close proximity). Our method demonstrated an increased accuracy by 5 to 8% and an increased kappa by 5%. Full article

(This article belongs to the Special Issue Artificial Intelligence on Brain–Computer Interface (BCI))

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