Evaluation of hybrid forecasting methods for organic Rankine cycle: Unsupervised learning-based outlier removal and partial mutual information-based feature selection

In this study, a new deep learning-based methodology is developed for P300 detection in brain computer interface (BCI) systems based on time-frequency (TF) features of EEG signals coupled to deep learning. The TF distributions can transform EEG signals to the TF images by simultaneous representation of time and frequency properties of the signal. However, they do not display the energy distribution of signals at different scales identically and their advantages may be greatly incorporated by using them together. Here, four TF images of single-trail EEG signal are computed and the concatenation of the TF (cTF) images of each signal is developed to be used as training data for a simple and lightweight deep learning-based classifier. The applied TF distributions are spectrogram, Wigner–Ville distribution, Morlet-scalogram, and Bertrand distribution. Performance of method is evaluated over limited data acquired from the normal and amyotrophic lateral sclerosis (ALS) datasets and accuracy of 96.56%, and 96.84% are achieved respectively, which is superior to the other comparing algorithms. Moreover, results of cross-subject classification indicate the promising ability of the method in eliminating calibration in BCI systems. Furthermore, the heat maps of the P300 and non-P300 classes are produced to explain important regions of cTF image for classifier decision and investigate which TF may help better classification. Results revealed the efficiency of cTF images for accurate P300 detection in simple structure classifiers having the advantage of fewer data and less memory requirement. This method can be employed in P300 speller BCI systems to improve the character recognition performance in.

An Interpretable Convolutional Neural Network for P300 Detection: Analysis of Time Frequency Features for Limited Data

Background: An open challenge of P300-based BCI systems focuses on recognizing ERP signals using a reduced number of trials with enough classification rate. Methods: Three novel methods based on Filter Bank and Canonical Correlation Analysis (CCA) are proposed for the recognition of P300 ERPs using a reduced number of trials. The proposed methods were evaluated with two freely available EEG datasets based on 6x6 speller and were compared with five standard methods: Mean-Amplitude, Step-Wise, Principal Component Analysis, Peak, and CCA. Results: The proposed methods outperform significantly standard algorithms for P300 identification with a maximum AUC of 0.93 and 0.98, and an average of 0.73 and 0.76, using a single trial. Conclusion: Proposed methods based on Filter Bank are robust for the identification of P300 using a reduced number of trials, which could be used in real-time BCI spellers for rehabilitation engineering. 

Enhancing P300 Detection Using a Band-Selective Filter Bank for a Visual P300 Speller

An efficient deep learning framework for P300 evoked related potential detection in EEG signal

Some classification studies of brain-computer interface (BCI) based on speech imagery show potential for improving communication skills in patients with amyotrophic lateral sclerosis (ALS). However, current research on speech imagery is limited in scope and primarily focuses on vowels or a few selected words. In this paper, we propose a complete research scheme for multi-character classification based on EEG signals derived from speech imagery. Firstly, we record 31 speech imagery contents, including 26 alphabets and 5 commonly used punctuation marks, from seven subjects using a 32-channel electroencephalogram (EEG) device. Secondly, we introduce the wavelet scattering transform (WST), which shares a structural resemblance to Convolutional Neural Networks (CNNs), for feature extraction. The WST is a knowledge-driven technique that preserves high-frequency information and maintains the deformation stability of EEG signals. To reduce the dimensionality of wavelet scattering coefficient features, we employ Kernel Principal Component Analysis (KPCA). Finally, the reduced features are fed into an Extreme Gradient Boosting (XGBoost) classifier within a multi-classification framework. The XGBoost classifier is optimized through hyperparameter tuning using grid search and 10-fold cross-validation, resulting in an average accuracy of 78.73% for the multi-character classification task. We utilize t-Distributed Stochastic Neighbor Embedding (t-SNE) technology to visualize the low-dimensional representation of multi-character speech imagery. This visualization effectively enables us to observe the clustering of similar characters. The experimental results demonstrate the effectiveness of our proposed multi-character classification scheme. Furthermore, our classification categories and accuracy exceed those reported in existing research.

A complete scheme for multi-character classification using EEG signals from speech imagery.

An efficient P300 detection algorithm based on Kernel Principal Component Analysis-Support Vector Machine

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An efficient P300 detection algorithm based on Kernel Principal Component Analysis-Support Vector Machine