916 research outputs found
Signal2Image Modules in Deep Neural Networks for EEG Classification
Deep learning has revolutionized computer vision utilizing the increased
availability of big data and the power of parallel computational units such as
graphical processing units. The vast majority of deep learning research is
conducted using images as training data, however the biomedical domain is rich
in physiological signals that are used for diagnosis and prediction problems.
It is still an open research question how to best utilize signals to train deep
neural networks.
In this paper we define the term Signal2Image (S2Is) as trainable or
non-trainable prefix modules that convert signals, such as
Electroencephalography (EEG), to image-like representations making them
suitable for training image-based deep neural networks defined as `base
models'. We compare the accuracy and time performance of four S2Is (`signal as
image', spectrogram, one and two layer Convolutional Neural Networks (CNNs))
combined with a set of `base models' (LeNet, AlexNet, VGGnet, ResNet, DenseNet)
along with the depth-wise and 1D variations of the latter. We also provide
empirical evidence that the one layer CNN S2I performs better in eleven out of
fifteen tested models than non-trainable S2Is for classifying EEG signals and
we present visual comparisons of the outputs of the S2Is.Comment: 4 pages, 2 figures, 1 table, EMBC 201
DGSD: Dynamical Graph Self-Distillation for EEG-Based Auditory Spatial Attention Detection
Auditory Attention Detection (AAD) aims to detect target speaker from brain
signals in a multi-speaker environment. Although EEG-based AAD methods have
shown promising results in recent years, current approaches primarily rely on
traditional convolutional neural network designed for processing Euclidean data
like images. This makes it challenging to handle EEG signals, which possess
non-Euclidean characteristics. In order to address this problem, this paper
proposes a dynamical graph self-distillation (DGSD) approach for AAD, which
does not require speech stimuli as input. Specifically, to effectively
represent the non-Euclidean properties of EEG signals, dynamical graph
convolutional networks are applied to represent the graph structure of EEG
signals, which can also extract crucial features related to auditory spatial
attention in EEG signals. In addition, to further improve AAD detection
performance, self-distillation, consisting of feature distillation and
hierarchical distillation strategies at each layer, is integrated. These
strategies leverage features and classification results from the deepest
network layers to guide the learning of shallow layers. Our experiments are
conducted on two publicly available datasets, KUL and DTU. Under a 1-second
time window, we achieve results of 90.0\% and 79.6\% accuracy on KUL and DTU,
respectively. We compare our DGSD method with competitive baselines, and the
experimental results indicate that the detection performance of our proposed
DGSD method is not only superior to the best reproducible baseline but also
significantly reduces the number of trainable parameters by approximately 100
times
Improving Emotion Recognition Systems by Exploiting the Spatial Information of EEG Sensors
Electroencephalography (EEG)-based emotion recognition is gaining increasing importance due to its potential applications in various scientific fields, ranging from psychophysiology to neuromarketing. A number of approaches have been proposed that use machine learning (ML) technology to achieve high recognition performance, which relies on engineering features from brain activity dynamics. Since ML performance can be improved by utilizing 2D feature representation that exploits the spatial relationships among the features, here we propose a novel input representation that involves re-arranging EEG features as an image that reflects the top view of the subject’s scalp. This approach enables emotion recognition through image-based ML methods such as pre-trained deep neural networks or "trained-from-scratch" convolutional neural networks. We have employed both of these techniques in our study to demonstrate the effectiveness of our proposed input representation. We also compare the recognition performance of these methods against state-of-the-art tabular data analysis approaches, which do not utilize the spatial relationships between the sensors. We test our proposed approach using two publicly available benchmark datasets for EEG-based emotion recognition tasks, namely DEAP and MAHNOB-HCI. Our results show that the "trained-from-scratch" convolutional neural network outperforms the best approaches in the literature, achieving 97.8% and 98.3% accuracy in valence and arousal classification on MAHNOB-HCI, and 91% and 90.4% on DEAP, respectively
EEGFuseNet: Hybrid Unsupervised Deep Feature Characterization and Fusion for High-Dimensional EEG With an Application to Emotion Recognition
How to effectively and efficiently extract valid and reliable features from high-dimensional electroencephalography (EEG), particularly how to fuse the spatial and temporal dynamic brain information into a better feature representation, is a critical issue in brain data analysis. Most current EEG studies work in a task driven manner and explore the valid EEG features with a supervised model, which would be limited by the given labels to a great extent. In this paper, we propose a practical hybrid unsupervised deep convolutional recurrent generative adversarial network based EEG feature characterization and fusion model, which is termed as EEGFuseNet. EEGFuseNet is trained in an unsupervised manner, and deep EEG features covering both spatial and temporal dynamics are automatically characterized. Comparing to the existing features, the characterized deep EEG features could be considered to be more generic and independent of any specific EEG task. The performance of the extracted deep and low-dimensional features by EEGFuseNet is carefully evaluated in an unsupervised emotion recognition application based on three public emotion databases. The results demonstrate the proposed EEGFuseNet is a robust and reliable model, which is easy to train and performs efficiently in the representation and fusion of dynamic EEG features. In particular, EEGFuseNet is established as an optimal unsupervised fusion model with promising cross-subject emotion recognition performance. It proves EEGFuseNet is capable of characterizing and fusing deep features that imply comparative cortical dynamic significance corresponding to the changing of different emotion states, and also demonstrates the possibility of realizing EEG based cross-subject emotion recognition in a pure unsupervised manner
Emotion Recognition from Electroencephalogram Signals based on Deep Neural Networks
Emotion recognition using deep learning methods through electroencephalogram (EEG) analysis has marked significant progress. Nevertheless, the complexities and time-intensive nature of EEG analysis present challenges. This study proposes an efficient EEG analysis method that foregoes feature extraction and sliding windows, instead employing one-dimensional Neural Networks for emotion classification. The analysis utilizes EEG signals from the Database for Emotion Analysis using Physiological Signals (DEAP) and focuses on thirteen EEG electrode positions closely associated with emotion changes. Three distinct Neural Models are explored for emotion classification: two Convolutional Neural Networks (CNN) and a combined approach using Convolutional Neural Networks and Long Short-Term Memory (CNN-LSTM). Additionally, two emotion labels are considered: four emotional ranges encompassing low arousal and low valence (LALV), low arousal and high valence (LAHV), high arousal and high valence (HAHV), and high arousal and low valence (HALV); and high valence (HV) and low valence (LV). Results demonstrate CNN_1 achieving an average accuracy of 97.7% for classifying four emotional ranges, CNN_2 with 97.1%, and CNN-LSTM reaching an impressive 99.5%. Notably, in classifying HV and LV labels, our methods attained remarkable accuracies of 100%, 98.8%, and 99.7% for CNN_1, CNN_2, and CNN-LSTM, respectively. The performance of our models surpasses that of previously reported studies, showcasing their potential as highly effective classifiers for emotion recognition using EEG signals
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