scholarly journals Spatio-Temporal Representation of an Electoencephalogram for Emotion Recognition Using a Three-Dimensional Convolutional Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3491 ◽  
Author(s):  
Jungchan Cho ◽  
Hyoseok Hwang

Emotion recognition plays an important role in the field of human–computer interaction (HCI). An electroencephalogram (EEG) is widely used to estimate human emotion owing to its convenience and mobility. Deep neural network (DNN) approaches using an EEG for emotion recognition have recently shown remarkable improvement in terms of their recognition accuracy. However, most studies in this field still require a separate process for extracting handcrafted features despite the ability of a DNN to extract meaningful features by itself. In this paper, we propose a novel method for recognizing an emotion based on the use of three-dimensional convolutional neural networks (3D CNNs), with an efficient representation of the spatio-temporal representations of EEG signals. First, we spatially reconstruct raw EEG signals represented as stacks of one-dimensional (1D) time series data to two-dimensional (2D) EEG frames according to the original electrode position. We then represent a 3D EEG stream by concatenating the 2D EEG frames to the time axis. These 3D reconstructions of the raw EEG signals can be efficiently combined with 3D CNNs, which have shown a remarkable feature representation from spatio-temporal data. Herein, we demonstrate the accuracy of the emotional classification of the proposed method through extensive experiments on the DEAP (a Dataset for Emotion Analysis using EEG, Physiological, and video signals) dataset. Experimental results show that the proposed method achieves a classification accuracy of 99.11%, 99.74%, and 99.73% in the binary classification of valence and arousal, and, in four-class classification, respectively. We investigate the spatio-temporal effectiveness of the proposed method by comparing it to several types of input methods with 2D/3D CNN. We then verify the best performing shape of both the kernel and input data experimentally. We verify that an efficient representation of an EEG and a network that fully takes advantage of the data characteristics can outperform methods that apply handcrafted features.

2019 ◽  
Vol 9 (12) ◽  
pp. 348 ◽  
Author(s):  
Ji-Hoon Jeong ◽  
Baek-Woon Yu ◽  
Dae-Hyeok Lee ◽  
Seong-Whan Lee

Non-invasive brain-computer interfaces (BCI) have been developed for recognizing human mental states with high accuracy and for decoding various types of mental conditions. In particular, accurately decoding a pilot’s mental state is a critical issue as more than 70% of aviation accidents are caused by human factors, such as fatigue or drowsiness. In this study, we report the classification of not only two mental states (i.e., alert and drowsy states) but also five drowsiness levels from electroencephalogram (EEG) signals. To the best of our knowledge, this approach is the first to classify drowsiness levels in detail using only EEG signals. We acquired EEG data from ten pilots in a simulated night flight environment. For accurate detection, we proposed a deep spatio-temporal convolutional bidirectional long short-term memory network (DSTCLN) model. We evaluated the classification performance using Karolinska sleepiness scale (KSS) values for two mental states and five drowsiness levels. The grand-averaged classification accuracies were 0.87 (±0.01) and 0.69 (±0.02), respectively. Hence, we demonstrated the feasibility of classifying five drowsiness levels with high accuracy using deep learning.


Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4736 ◽  
Author(s):  
Heekyung Yang ◽  
Jongdae Han ◽  
Kyungha Min

We present a multi-column CNN-based model for emotion recognition from EEG signals. Recently, a deep neural network is widely employed for extracting features and recognizing emotions from various biosignals including EEG signals. A decision from a single CNN-based emotion recognizing module shows improved accuracy than the conventional handcrafted feature-based modules. To further improve the accuracy of the CNN-based modules, we devise a multi-column structured model, whose decision is produced by a weighted sum of the decisions from individual recognizing modules. We apply the model to EEG signals from DEAP dataset for comparison and demonstrate the improved accuracy of our model.


Author(s):  
Shaznoor Shakira Saharuddin ◽  
Norhanifah Murli ◽  
Muhammad Azani Hasibuan

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