GA for feature selection of EEG heterogeneous data

The electroencephalographic (EEG) signals provide highly informative data on brain activities and functions. However, their heterogeneity and high dimensionality may represent an obstacle for their interpretation. The introduction of a priori knowledge seems the best option to mitigate high dimensionality problems, but could lose some information and patterns present in the data, while data heterogeneity remains an open issue that often makes generalization difficult. In this study, we propose a genetic algorithm (GA) for feature selection that can be used with a supervised or unsupervised approach. Our proposal considers three different fitness functions without relying on expert knowledge. Starting from two publicly available datasets on cognitive workload and motor movement/imagery, the EEG signals are processed, normalized and their features computed in the time, frequency and time-frequency domains. The feature vector selection is performed by applying our GA proposal and compared with two benchmarking techniques. The results show that different combinations of our proposal achieve better results in respect to the benchmark in terms of overall performance and feature reduction. Moreover, the proposed GA, based on a novel fitness function here presented, outperforms the benchmark when the two different datasets considered are merged together, showing the effectiveness of our proposal on heterogeneous data.Comment: submitted to Expert Systems with Application

Noise Reduction of EEG Signals Using Autoencoders Built Upon GRU based RNN Layers

Understanding the cognitive and functional behaviour of the brain by its electrical activity is an important area of research. Electroencephalography (EEG) is a method that measures and record electrical activities of the brain from the scalp. It has been used for pathology analysis, emotion recognition, clinical and cognitive research, diagnosing various neurological and psychiatric disorders and for other applications. Since the EEG signals are sensitive to activities other than the brain ones, such as eye blinking, eye movement, head movement, etc., it is not possible to record EEG signals without any noise. Thus, it is very important to use an efficient noise reduction technique to get more accurate recordings. Numerous traditional techniques such as Principal Component Analysis (PCA), Independent Component Analysis (ICA), wavelet transformations and machine learning techniques were proposed for reducing the noise in EEG signals. The aim of this paper is to investigate the effectiveness of stacked autoencoders built upon Gated Recurrent Unit (GRU) based Recurrent Neural Network (RNN) layers (GRU-AE) against PCA. To achieve this, Harrell-Davis decile values for the reconstructed signals’ signal-to- noise ratio distributions were compared and it was found that the GRU-AE outperformed PCA for noise reduction of EEG signals

Decoding Neural Signals with Computational Models: A Systematic Review of Invasive BMI

There are significant milestones in modern human's civilization in which mankind stepped into a different level of life with a new spectrum of possibilities and comfort. From fire-lighting technology and wheeled wagons to writing, electricity and the Internet, each one changed our lives dramatically. In this paper, we take a deep look into the invasive Brain Machine Interface (BMI), an ambitious and cutting-edge technology which has the potential to be another important milestone in human civilization. Not only beneficial for patients with severe medical conditions, the invasive BMI technology can significantly impact different technologies and almost every aspect of human's life. We review the biological and engineering concepts that underpin the implementation of BMI applications. There are various essential techniques that are necessary for making invasive BMI applications a reality. We review these through providing an analysis of (i) possible applications of invasive BMI technology, (ii) the methods and devices for detecting and decoding brain signals, as well as (iii) possible options for stimulating signals into human's brain. Finally, we discuss the challenges and opportunities of invasive BMI for further development in the area.Comment: 51 pages, 14 figures, review articl

Brainwave-Based Human Emotion Estimation using Deep Neural Network Models for Biofeedback

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonEmotion is a state that comprehensively represents human feeling, thought and behavior, thus takes an important role in interpersonal human communication. Emotion estimation aims to automatically discriminate different emotional states by using physiological and nonphysiological signals acquired from human to achieve effective communication and interaction between human and machines. Brainwaves-Based Emotion Estimation is one of the most common used and efficient methods for emotion estimation research. The technology reveals a great role for human emotional disorder treatment, brain computer interface for disabilities, entertainment and many other research areas. In this thesis, various methods, schemes and frameworks are presented for Electroencephalogram (EEG) based human emotion estimation. Firstly, a hybrid dimension featurere duction scheme is presented using a total of 14 different features extracted from EEG recordings. The scheme combines these distinct features in the feature space using both supervised and unsupervised feature selection processes. Maximum Relevance Minimum Redundancy (mRMR) is applied to re-order the combined features into max-relevance with the emotion labels and min-redundancy of each feature. The generated features are further reduced with Principal Component Analysis (PCA) for extracting the principal components. Experimental results show that the proposed work outperforms the state-of-art methods using the same settings at the publicly available Database for Emotional Analysis using Physiological Signals (DEAP) data set. Secondly, a disentangled adaptive noise learning β-Variational autoencoder (VAE) combinewithlongshorttermmemory(LSTM)modelwasproposedfortheemotionrecognition based on EEG recordings. The experiment is also based on the EEG emotion public DEAPdataset. At first, the EEG time-series data are transformed into the Video-like EEG image data through the Azimuthal Equidistant Projection (AEP) to original EEG-sensor 3-D coordinates to perform 2-D projected locations of electrodes. Then Clough-Tocher scheme is applied for interpolating the scattered power measurements over the scalp and for estimating the values in-between the electrodes over a 32x32 mesh. After that, the βVAE LSTM algorithm is used to estimate the accuracy of the quadratic (arousal-valence) classification. The comparison between the β VAE-LSTM model and other classic methods is conducted at the same experimental setting that shows that the proposed model is effective. Finally, a novel real-time emotion detection system based on the EEG signals from a portable headband was presented, integrated into the interactive film ‘RIOT’. At first, the requirement of the interactive film was collected and the protocol for data collection using a portable EEG sensor (Emotiv Epoc) was designed. Then, a portable EEG emotion database (PEED) is built from 10 participants with the emotion labels using both self-reporting and video annotation tools. After that, various feature extraction, feature selection, validation scheme and classification methods are explored to build a practical system for the real-time detection. In the end, the emotion detection system is trained and integrated into the interactive film for real-time implementation and fully evaluated. The experimental results demonstrate the system with satisfied emotion detection accuracy and real-time performance

Semi-Supervised End-To-End Contrastive Learning For Time Series Classification

Time series classification is a critical task in various domains, such as finance, healthcare, and sensor data analysis. Unsupervised contrastive learning has garnered significant interest in learning effective representations from time series data with limited labels. The prevalent approach in existing contrastive learning methods consists of two separate stages: pre-training the encoder on unlabeled datasets and fine-tuning the well-trained model on a small-scale labeled dataset. However, such two-stage approaches suffer from several shortcomings, such as the inability of unsupervised pre-training contrastive loss to directly affect downstream fine-tuning classifiers, and the lack of exploiting the classification loss which is guided by valuable ground truth. In this paper, we propose an end-to-end model called SLOTS (Semi-supervised Learning fOr Time clasSification). SLOTS receives semi-labeled datasets, comprising a large number of unlabeled samples and a small proportion of labeled samples, and maps them to an embedding space through an encoder. We calculate not only the unsupervised contrastive loss but also measure the supervised contrastive loss on the samples with ground truth. The learned embeddings are fed into a classifier, and the classification loss is calculated using the available true labels. The unsupervised, supervised contrastive losses and classification loss are jointly used to optimize the encoder and classifier. We evaluate SLOTS by comparing it with ten state-of-the-art methods across five datasets. The results demonstrate that SLOTS is a simple yet effective framework. When compared to the two-stage framework, our end-to-end SLOTS utilizes the same input data, consumes a similar computational cost, but delivers significantly improved performance. We release code and datasets at https://anonymous.4open.science/r/SLOTS-242E.Comment: Submitted to NeurIPS 202