EEG-based image classification via a region-level stacked bi-directional deep learning framework

Salient Arithmetic Data Extraction from Brain Activity via an Improved Deep Network

Interpretation of neural activity in response to stimulations received from the surrounding environment is necessary to realize automatic brain decoding. Analyzing the brain recordings corresponding to visual stimulation helps to infer the effects of perception occurring by vision on brain activity. In this paper, the impact of arithmetic concepts on vision-related brain records has been considered and an efficient convolutional neural network-based generative adversarial network (CNN-GAN) is proposed to map the electroencephalogram (EEG) to salient parts of the image stimuli. The first part of the proposed network consists of depth-wise one-dimensional convolution layers to classify the brain signals into 10 different categories according to Modified National Institute of Standards and Technology (MNIST) image digits. The output of the CNN part is fed forward to a fine-tuned GAN in the proposed model. The performance of the proposed CNN part is evaluated via the visually provoked 14-channel MindBigData recorded by David Vivancos, corresponding to images of 10 digits. An average accuracy of 95.4% is obtained for the CNN part for classification. The performance of the proposed CNN-GAN is evaluated based on saliency metrics of SSIM and CC equal to 92.9% and 97.28%, respectively. Furthermore, the EEG-based reconstruction of MNIST digits is accomplished by transferring and tuning the improved CNN-GAN's trained weights.

Brain-guided manifold transferring to improve the performance of spiking neural networks in image classification

Spiking neural networks (SNNs), as the third generation of neural networks, are based on biological models of human brain neurons. In this work, a shallow SNN plays the role of an explicit image decoder in the image classification. An LSTM-based EEG encoder is used to construct the EEG-based feature space, which is a discriminative space in viewpoint of classification accuracy by SVM. Then, the visual feature vectors extracted from SNN is mapped to the EEG-based discriminative features space by manifold transferring based on mutual k-Nearest Neighbors (Mk-NN MT). This proposed "Brain-guided system" improves the separability of the SNN-based visual feature space. In the test phase, the spike patterns extracted by SNN from the input image is mapped to LSTM-based EEG feature space, and then classified without need for the EEG signals. The SNN-based image encoder is trained by the conversion method and the results are evaluated and compared with other training methods on the challenging small ImageNet-EEG dataset. Experimental results show that the proposed transferring the manifold of the SNN-based feature space to LSTM-based EEG feature space leads to 14.25% improvement at most in the accuracy of image classification. Thus, embedding SNN in the brain-guided system which is trained on a small set, improves its performance in image classification.

CollectiveNet-AltSpec: A collective concurrent CNN architecture of alternate specifications for EEG media perception and emotion tracing aided by multi-domain feature-augmentation

Enhancing computability of cerebral recordings and connections made with human/non-human brain have been on track and are expected to propel in our current era. An effective contribution towards said ends is improving accuracy of attempts at discerning intricate phenomena taking place within human brain. Here and in two different capacities of experiments, we attempt to distinguish cerebral perceptions shaped and affective states surfaced during observation of samples of media incorporating distinct audio-visual and emotional contents, through employing electroencephalograph/EEG recorded sessions of two reputable datasets of DEAP and SEED. Here we introduce AltSpec(E3) the inceptive form of CollectiveNet intelligent computational architectures employing collective and concurrent multi-spec analysis to exploit complex patterns in complex data-structures. This processing technique uses a full array of diversification protocols with multifarious parts enabling surgical levels of optimization while integrating a holistic analysis of patterns. Data-structures designed here contain multi-electrode neuroinformatic and neurocognitive features studying emotion reactions and attentive patterns. These spatially and temporally featured 2D/3D constructs of domain-augmented data are eventually AI-processed and outputs are defragmented forming one definitive judgement. The media-perception tracing is arguably first of its kind, at least when implemented on mentioned datasets. Backed by this multi-directional approach and in subject-independent configurations for perception-tracing on 5-media-class basis, mean accuracies of 81.00% and 68.93% were obtained on DEAP and SEED, respectively. We also managed to classify emotions with accuracies of 61.59% and 66.21% in cross-dataset validation followed by 81.47% and 88.12% in cross-subject validation settings trained on DEAP and SEED, consecutively.

Status of deep learning for EEG-based brain-computer interface applications

In the previous decade, breakthroughs in the central nervous system bioinformatics and computational innovation have prompted significant developments in brain-computer interface (BCI), elevating it to the forefront of applied science and research. BCI revitalization enables neurorehabilitation strategies for physically disabled patients (e.g., disabled patients and hemiplegia) and patients with brain injury (e.g., patients with stroke). Different methods have been developed for electroencephalogram (EEG)-based BCI applications. Due to the lack of a large set of EEG data, methods using matrix factorization and machine learning were the most popular. However, things have changed recently because a number of large, high-quality EEG datasets are now being made public and used in deep learning-based BCI applications. On the other hand, deep learning is demonstrating great prospects for solving complex relevant tasks such as motor imagery classification, epileptic seizure detection, and driver attention recognition using EEG data. Researchers are doing a lot of work on deep learning-based approaches in the BCI field right now. Moreover, there is a great demand for a study that emphasizes only deep learning models for EEG-based BCI applications. Therefore, we introduce this study to the recent proposed deep learning-based approaches in BCI using EEG data (from 2017 to 2022). The main differences, such as merits, drawbacks, and applications are introduced. Furthermore, we point out current challenges and the directions for future studies. We argue that this review study will help the EEG research community in their future research.

Early Stroke Prediction Methods for Prevention of Strokes

The emergence of the latest technologies gives rise to the usage of noninvasive techniques for assisting health-care systems. Amongst the four major cardiovascular diseases, stroke is one of the most dangerous and life-threatening disease, but the life of a patient can be saved if the stroke is detected during early stage. The literature reveals that the patients always experience ministrokes which are also known as transient ischemic attacks (TIA) before experiencing the actual attack of the stroke. Most of the literature work is based on the MRI and CT scan images for classifying the cardiovascular diseases including a stroke which is an expensive approach for diagnosis of early strokes. In India where cases of strokes are rising, there is a need to explore noninvasive cheap methods for the diagnosis of early strokes. Hence, this problem has motivated us to conduct the study presented in this paper. A noninvasive approach for the early diagnosis of the strokes is proposed. The cascaded prediction algorithms are time-consuming in producing the results and cannot work on the raw data and without making use of the properties of EEG. Therefore, the objective of this paper is to devise mechanisms to forecast strokes on the basis of processed EEG data. This paper is proposing time series-based approaches such as LSTM, biLSTM, GRU, and FFNN that can handle time series-based predictions to make useful decisions. The experimental research outcome reveals that all the algorithms taken up for the research study perform well on the prediction problem of early stroke detection, but GRU performs the best with 95.6% accuracy, whereas biLSTM gives 91% accuracy and LSTM gives 87% accuracy and FFNN gives 83% accuracy. The experimental outcome is able to measure the brain waves to predict the signs of strokes. The findings can certainly assist the physicians to detect the stroke at early stages to save the lives of the patients.

Electroencephalogram Image under Complex Domain Analysis Algorithm to Analyze Neurological Status Epilepticus and Poor Prognostic Factors of Children

This study was to adopt the electroencephalogram (EEG) image to analyze the neurological status epilepticus (SE) and adverse prognostic factors of children using the complex domain analysis algorithm, aiming at providing a theoretical basis for the clinical treatment of children with SE. 24-hour EEG was adopted to diagnose 197 children with SE. The patients were divided into an experimental group (100 cases) and a control group (97 cases) using a random number table method. The EEGs of children in the experimental group were analyzed using the compound domain analysis algorithm, and those in the control group were diagnosed by a professional doctor. The indicators of children in two groups were compared to analyze the effect of the compound domain analysis algorithm in diagnosing diseases through EEG. The prognostic scores of 197 children were scored one month after they were diagnosed, treated, and discharged, and the adverse prognostic factors were analyzed. As a result, EEG can accurately and effectively analyze the brain diseases in children. The sensitivity and specificity of the complex domain analysis algorithm for the detection of epilepsy EEG were much higher than those of the EEG automatic detection algorithm based on time-domain waveform similarity and the EEG automatic detection algorithm based on convolutional neural network (CNN), and the average running time was opposite, showing obvious difference (P < 0.05).The average accuracy, sensitivity, and specificity of children in the experimental group were 96.11%, 97.10%, and 95.19%, respectively; and those in the control group were 88.83%, 90.14%, and 87.82%, respectively, so there was an obvious difference in accuracy between two groups (P < 0.05). There were 57 cases with good prognosis and 140 cases with poor prognosis; there were 70 males with good prognosis and 19 poor prognoses and 69 women with good prognosis and 19 poor prognoses. Among 121 patients with infections, 84 cases had good prognosis and 37 cases had poor prognosis; 39 cases of irregular medication had good prognosis in 31 cases and a poor prognosis in 8 cases; and 37 cases had no obvious cause, including 25 cases with good prognosis and 12 cases with poor prognosis. In short, the EEG diagnosis and treatment effect of the compound domain analysis algorithm were better than those of professional doctors; the gender of the patient had no effect on the poor prognosis, and the pathogenic factors had an impact on the poor prognosis of the patient.

Siamese Neural Networks for EEG-based Brain-computer Interfaces

Motivated by the inconceivable capability of human brain in simultaneously processing multi-modal signals and its real-time feedback to the outer world events, there has been a surge of interest in establishing a communication bridge between the human brain and a computer, which are referred to as Brain-computer Interfaces (BCI). To this aim, monitoring the electrical activity of brain through Electroencephalogram (EEG) has emerged as the prime choice for BCI systems. To discover the underlying and specific features of brain signals for different mental tasks, a considerable number of research works are developed based on statistical and data-driven techniques. However, a major bottleneck in development of practical and commercial BCI systems is their limited performance when the number of mental tasks for classification is increased. In this work, we propose a new EEG processing and feature extraction paradigm based on Siamese neural networks, which can be conveniently merged and scaled up for multi-class problems. The idea of Siamese networks is to train a double-input neural network based on a contrastive loss-function, which provides the capability of verifying if two input EEG trials are from the same class or not. In this work, a Siamese architecture, which is developed based on Convolutional Neural Networks (CNN) and provides a binary output on the similarity of two inputs, is combined with One vs. Rest (OVR) and One vs. One (OVO) techniques to scale up for multi-class problems. The efficacy of this architecture is evaluated on a 4-class Motor Imagery (MI) dataset from BCI Competition IV_2a and the results suggest a promising performance compared to its counterparts.