Energy Distribution of EEG Signals: EEG Signal Wavelet-Neural Network Classifier

Multiclass Common Spatial Pattern for EEG based Brain Computer Interface with Adaptive Learning Classifier

In Brain Computer Interface (BCI), data generated from Electroencephalogram (EEG) is non-stationary with low signal to noise ratio and contaminated with artifacts. Common Spatial Pattern (CSP) algorithm has been proved to be effective in BCI for extracting features in motor imagery tasks, but it is prone to overfitting. Many algorithms have been devised to regularize CSP for two class problem, however they have not been effective when applied to multiclass CSP. Outliers present in data affect extracted CSP features and reduces performance of the system. In addition to this non-stationarity pre…

A Many Objective Optimization Approach for Transfer Learning in EEG Classification

In Brain-Computer Interfacing (BCI), due to inter-subject non-stationarities of electroencephalogram (EEG), classifiers are trained and tested using EEG from the same subject. When physical disabilities bottleneck the natural modality of performing a task, acquisition of ample training data is difficult which practically obstructs classifier training. Previous works have tackled this problem by generalizing the feature space amongst multiple subjects including the test subject. This work aims at knowledge transfer to classify EEG of the target subject using a classifier trained with the EEG of…

基于新型熵差的EEG通道选择技术用于ADHD自动检测

Novel entropy difference-based EEG channel selection technique for automated detection of ADHD

本研究针对儿童注意缺陷多动障碍（ADHD）的自动检测问题，提出基于熵差（EnD）的EEG通道选择方法，结合多类特征与分类器，在公共数据库上达99.29%准确率，性能优于传统熵基通道选择方法。

基于神经气体特征的PSO模糊XGBoost分类器在脑电信号情绪识别中的应用

PSO Fuzzy XGBoost Classifier Boosted with Neural Gas Features on EEG Signals in Emotion Recognition

本文提出将神经气体网络、PSO与模糊逻辑集成到XGBoost中，用于脑电信号情绪识别，实验表明该方法在多数分类器上优于其他特征选择技术，显著提升了识别准确率。

A Deep Evolutionary Approach to Bioinspired Classifier Optimisation for Brain-Machine Interaction

This study suggests a new approach to EEG data classification by exploring the idea of using evolutionary computation to both select useful discriminative EEG features and optimise the topology of Artificial Neural Networks. An evolutionary algorithm is applied to select the most informative features from an initial set of 2550 EEG statistical features. Optimisation of a Multilayer Perceptron (MLP) is performed with an evolutionary approach before classification to estimate the best hyperparameters of the network. Deep learning and tuning with Long Short-Term Memory (LSTM) are also explored, a…

CwA-T：一种用于EEG异常检测的通道级自动编码器与Transformer结合框架

CwA-T: A Channelwise AutoEncoder with Transformer for EEG Abnormality Detection

本文提出CwA-T框架，结合通道级CNN自动编码器与单头Transformer分类器，有效压缩EEG信号并保留生物特征，实现高精度异常检测，优于EEGNet等基线模型。