Extended ICA and M-CSP with BiLSTM towards improved classification of EEG signals

A novel deep learning model combining 3DCNN-CapsNet and hierarchical attention mechanism for EEG emotion recognition

Emotion recognition plays a key role in the field of human-computer interaction. Classifying and predicting human emotions using electroencephalogram (EEG) signals has consistently been a challenging research area. Recently, with the increasing application of deep learning methods such as convolutional neural network (CNN) and channel attention mechanism (CA). The recognition accuracy of emotion recognition methods has already reached an outstanding level. However, CNN and its derivatives have the defect that the sensory field of view is small and can only extract local features. The traditional channel attention mechanism only focuses on the correlation between different channels and assigns weights to each channel according to its contribution to the emotion recognition task, ignoring the fact that different EEG frequency bands in the same channel signal also have different contributions to the task. To address the above-mentioned problems , this paper propose HA-CapsNet, a novel end-to-end model combining 3DCNN-CapsNet with a Hierarchical Attention mechanism. This model captures both inter-channel correlations and the contribution of each frequency band. Additionally, the capsule network in 3DCNN-CapsNet extracts more spatial feature information compared to conventional CNNs. Our HA-CapsNet achieves recognition accuracies of 97.40%, 97.20%, and 97.60% on the DEAP dataset, and 95.80%, 96.10%, and 96.30% on the DREAMER dataset, outperforming state-of-the-art methods with the smallest variance. Furthermore, experiments removing channels from the DEAP and DREAMER datasets in ascending order of their hierarchical attention weights showed that even with fewer channels, the model maintained strong recognition performance. This demonstrates HA-CapsNet's low dependence on large datasets and its suitability for lightweight EEG devices, promoting advancements in EEG device development.

Paraphrase detection for Urdu language text using fine-tune BiLSTM framework

Automated paraphrase detection is crucial for natural language processing (NL) applications like text summarization, plagiarism detection, and question-answering systems. Detecting paraphrases in Urdu text remains challenging due to the language's complex morphology, distinctive script, and lack of resources such as labelled datasets, pre-trained models, and tailored NLP tools. This research proposes a novel bidirectional long short-term memory (BiLSTM) framework to address Urdu paraphrase detection's intricacies. Our approach employs word embeddings and text preprocessing techniques like tokenization, stop-word removal, and label encoding to effectively handle Urdu's morphological variations. The BiLSTM network sequentially processes the input, leveraging both forward and backward contextual information to encode the complex syntactic and semantic patterns inherent in Urdu text. An essential contribution of this work is the creation of a large-scale Urdu Paraphrased Corpus (UPC) comprising 400,000 potential sentence pair duplicates, with 150,000 pairs manually identified as paraphrases. Our findings reveal a significant improvement in paraphrase detection performance compared to existing methods. We provide insights into the underlying linguistic features and patterns that contribute to the robustness of our framework. This resource facilitates training and evaluating Urdu paraphrase detection models. Experimental evaluations on the custom UPC dataset demonstrate our BiLSTM model's superiority, achieving 94.14% accuracy and outperforming state-of-the-art methods like CNN (83.43%) and LSTM (88.09%). Our model attains an impressive 95.34% accuracy on the benchmark Quora dataset. Furthermore, we incorporate a comprehensive linguistic rule engine to handle exceptional cases during paraphrase analysis, ensuring robust performance across diverse contexts.

Channel attention convolutional aggregation network based on video-level features for EEG emotion recognition

Electroencephalogram (EEG) emotion recognition plays a vital role in affective computing. A limitation of the EEG emotion recognition task is that the features of multiple domains are rarely included in the analysis simultaneously because of the lack of an effective feature organization form. This paper proposes a video-level feature organization method to effectively organize the temporal, frequency and spatial domain features. In addition, a deep neural network, Channel Attention Convolutional Aggregation Network, is designed to explore deeper emotional information from video-level features. The network uses a channel attention mechanism to adaptively captures critical EEG frequency bands. Then the frame-level representation of each time point is obtained by multi-layer convolution. Finally, the frame-level features are aggregated through NeXtVLAD to learn the time-sequence-related features. The method proposed in this paper achieves the best classification performance in SEED and DEAP datasets. The mean accuracy and standard deviation of the SEED dataset are 95.80% and 2.04%. In the DEAP dataset, the average accuracy with the standard deviation of arousal and valence are 98.97% ± 1.13% and 98.98% ± 0.98%, respectively. The experimental results show that our approach based on video-level features is effective for EEG emotion recognition tasks.

Diagnostic biomarker discovery from brain EEG data using LSTM, reservoir-SNN, and NeuCube methods in a pilot study comparing epilepsy and migraine

The study introduces a new online spike encoding algorithm for spiking neural networks (SNN) and suggests new methods for learning and identifying diagnostic biomarkers using three prominent deep learning neural network models: deep BiLSTM, reservoir SNN, and NeuCube. EEG data from datasets related to epilepsy, migraine, and healthy subjects are employed. Results reveal that BiLSTM hidden neurons capture biological significance, while reservoir SNN activities and NeuCube spiking dynamics identify EEG channels as diagnostic biomarkers. BiLSTM and reservoir SNN achieve 90 and 85% classification accuracy, while NeuCube achieves 97%, all methods pinpointing potential biomarkers like T6, F7, C4, and F8. The research bears implications for refining online EEG classification, analysis, and early brain state diagnosis, enhancing AI models with interpretability and discovery. The proposed techniques hold promise for streamlined brain-computer interfaces and clinical applications, representing a significant advancement in pattern discovery across the three most popular neural network methods for addressing a crucial problem. Further research is planned to study how early can these diagnostic biomarkers predict an onset of brain states.

Capped L21-norm-based common spatial patterns for EEG signals classification applicable to BCI systems

The common spatial patterns (CSP) technique is an effective strategy for the classification of multichannel electroencephalogram (EEG) signals. However, the objective function expression of the conventional CSP algorithm is based on the L2-norm, which makes the performance of the method easily affected by outliers and noise. In this paper, we consider a new extension to CSP, which is termed capped L21-norm-based common spatial patterns (CCSP-L21), by using the capped L21-norm rather than the L2-norm for robust modeling. L21-norm considers the L1-norm sum which largely alleviates the influence of outliers and noise for the sake of robustness. The capped norm is further used to mitigate the effects of extreme outliers whose signal amplitude is much higher than that of the normal signal. Moreover, a non-greedy iterative procedure is derived to solve the proposed objective function. The experimental results show that the proposed method achieves the highest average recognition rates on the three real data sets of BCI competitions, which are 91.67%, 85.07%, and 82.04%, respectively. Capped L21-norm-based common spatial patterns-a robust model for EEG signals classification.