scholarly journals Hand and Leg Movement Prediction using EEG Signal by Stacked Deep Auto Encoder

2019 ◽  
Vol 5 (10) ◽  
pp. 36-48
Author(s):  
Kulsheet Kaur Virdi ◽  
Satish Pawar
Keyword(s):  
Research Work ◽  
Mental States ◽  
Research Area ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Left Hand ◽  
Leg Movement ◽  
Brain Computer Interfacing ◽  
Selection Of

Brain Computer Interface (BCI) is device that enables the use of the brain’s neural activity to communicate with others or to control machines, artificial limbs, or robots without direct physical movements. Brain–computer interfacing is an uprising field of research wherever signals extracted from the human brain are used for deciding and generation of control signals. Selection of the most appropriate classifier to find the mental states from electroencephalography (EEG) signal is an open research area due to the signal’s non-stationary and ergodic nature. In this research work the proposed algorithm is designed to solve an important application in BCI where left hand forward–backward movements and right hand forward-backward movements as well as left leg movement and right leg movement are needed to be classified. Features are extracted from these datasets to classify the type of movements. A staked Deepauto encoder is used for classification of hand and leg movements and compared with other classifiers. The accuracy of stacked deepauto encoder is better with respect to other classifiers in terms of classification of hand and leg movement of EEG signals.

scholarly journals Hybrid Method of Automated EEG Signals’ Selection Using Reversed Correlation Algorithm for Improved Classification of Emotions

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7083
Author(s):  
Agnieszka Wosiak ◽  
Aleksandra Dura
Keyword(s):  
Hybrid Approach ◽  
Research Area ◽  
Eeg Signals ◽  
Computer Interfaces ◽  
Correlation Algorithm ◽  
Efficient Information ◽  
Important Research Area ◽  
Valence And Arousal ◽  
Selection Of

Based on the growing interest in encephalography to enhance human–computer interaction (HCI) and develop brain–computer interfaces (BCIs) for control and monitoring applications, efficient information retrieval from EEG sensors is of great importance. It is difficult due to noise from the internal and external artifacts and physiological interferences. The enhancement of the EEG-based emotion recognition processes can be achieved by selecting features that should be taken into account in further analysis. Therefore, the automatic feature selection of EEG signals is an important research area. We propose a multistep hybrid approach incorporating the Reversed Correlation Algorithm for automated frequency band—electrode combinations selection. Our method is simple to use and significantly reduces the number of sensors to only three channels. The proposed method has been verified by experiments performed on the DEAP dataset. The obtained effects have been evaluated regarding the accuracy of two emotions—valence and arousal. In comparison to other research studies, our method achieved classification results that were 4.20–8.44% greater. Moreover, it can be perceived as a universal EEG signal classification technique, as it belongs to unsupervised methods.

scholarly journals Classification of Drowsiness Levels Based on a Deep Spatio-Temporal Convolutional Bidirectional LSTM Network Using Electroencephalography Signals

2019 ◽  
Vol 9 (12) ◽  
pp. 348 ◽  
Author(s):  
Ji-Hoon Jeong ◽  
Baek-Woon Yu ◽  
Dae-Hyeok Lee ◽  
Seong-Whan Lee
Keyword(s):  
Short Term Memory ◽  
Mental States ◽  
Critical Issue ◽  
High Accuracy ◽  
Classification Performance ◽  
Eeg Signals ◽  
Aviation Accidents ◽  
Spatio Temporal ◽  
Lstm Network

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.

scholarly journals Motor Imagery EEG Classification Using Capsule Networks

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2854 ◽  
Author(s):  
Kwon-Woo Ha ◽  
Jin-Woo Jeong
Keyword(s):  
Motor Imagery ◽  
Learning Approaches ◽  
Eeg Signals ◽  
Left Hand ◽  
Computer Interfaces ◽  
Conventional Machine ◽  
Short Time ◽  
Electroencephalogram Eeg ◽  
Target Data

Various convolutional neural network (CNN)-based approaches have been recently proposed to improve the performance of motor imagery based-brain-computer interfaces (BCIs). However, the classification accuracy of CNNs is compromised when target data are distorted. Specifically for motor imagery electroencephalogram (EEG), the measured signals, even from the same person, are not consistent and can be significantly distorted. To overcome these limitations, we propose to apply a capsule network (CapsNet) for learning various properties of EEG signals, thereby achieving better and more robust performance than previous CNN methods. The proposed CapsNet-based framework classifies the two-class motor imagery, namely right-hand and left-hand movements. The motor imagery EEG signals are first transformed into 2D images using the short-time Fourier transform (STFT) algorithm and then used for training and testing the capsule network. The performance of the proposed framework was evaluated on the BCI competition IV 2b dataset. The proposed framework outperformed state-of-the-art CNN-based methods and various conventional machine learning approaches. The experimental results demonstrate the feasibility of the proposed approach for classification of motor imagery EEG signals.

scholarly journals Data Mining Based Approach for Evaluation of EEG Signals for Epilepsy Detection

2020 ◽  
Vol 17 ◽  
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Physiological Signals ◽  
Alternative Formulation ◽  
Eeg Signal ◽  
Detection Problem ◽  
Eeg Signals ◽  
Epileptic Patients ◽  
To Come

The objective of this proposed research is to come up with a general methodology for classification of time series events, and to apply that methodology to the analysis of physiological signals recorded from epileptic patients for seizure analysis depending on EEG signal. In contrast to previous works, this research considered an alternative formulation of seizure analysis as a detection problem. This approach offers a good treatment of seizure detection

scholarly journals Brain and Body Emotional Responses: Multimodal Approximation for Valence Classification

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 313 ◽  
Author(s):  
Jennifer Sorinas ◽  
Jose Manuel Ferrández ◽  
Eduardo Fernandez
Keyword(s):  
Emotional Responses ◽  
System Level ◽  
Ecg Signal ◽  
Eeg Signal ◽  
Multimodal Approach ◽  
Men And Women ◽  
Affective Applications ◽  
The Relationship ◽  
Selection Of

In order to develop more precise and functional affective applications, it is necessary to achieve a balance between the psychology and the engineering applied to emotions. Signals from the central and peripheral nervous systems have been used for emotion recognition purposes, however, their operation and the relationship between them remains unknown. In this context, in the present work, we have tried to approach the study of the psychobiology of both systems in order to generate a computational model for the recognition of emotions in the dimension of valence. To this end, the electroencephalography (EEG) signal, electrocardiography (ECG) signal and skin temperature of 24 subjects have been studied. Each methodology has been evaluated individually, finding characteristic patterns of positive and negative emotions in each of them. After feature selection of each methodology, the results of the classification showed that, although the classification of emotions is possible at both central and peripheral levels, the multimodal approach did not improve the results obtained through the EEG alone. In addition, differences have been observed between cerebral and peripheral responses in the processing of emotions by separating the sample by sex; though, the differences between men and women were only notable at the peripheral nervous system level.

Effect of Wavelet Packet Log Energy Entropy on Electroencephalogram (EEG) Signals

2015 ◽  
Vol 4 (1) ◽  
pp. 32-43 ◽  
Author(s):  
S. Raghu ◽  
N. Sriraam ◽  
G. Pradeep Kumar
Keyword(s):  
Epileptic Seizure ◽  
Linear Time ◽  
Wavelet Packet ◽  
Research Work ◽  
Eeg Signals ◽  
Time Frequency ◽  
Energy Entropy ◽  
Evoked Response Potential ◽  
Electroencephalogram Eeg ◽  
Selection Of

The scaling behavior of human electroencephalogram (EEG) signals is well exploited by appropriate extraction of time – frequency domain and entropy based features. Such measurable inherently helps understanding the neurophysiological phenomenon of brain as well as its associated cortical activities. Being a non-linear time series, EEG's are assumed to be fragment of fluctuations. Several attempts have been made to study the EEG signals for clinical applications such as epileptic seizure detection, evoked response potential recognition, tumor detection, identification of alcoholics and so on. In all such applications appropriate selection of feature parameter plays an important role in discriminating normal EEG from abnormal. In the recent past one can find the importance of wavelet and wavelet packet towards EEG analysis. This proposed research work investigates the effect of wavelet packet log energy entropy on EEG signals. Entropy being the measure of relative information, the proposed study attempts to discriminate the normal EEGs from abnormal EEG's by employing the log energy entropy features. For better brevity, this study restricts to the analysis of epileptic seizure from normal EEGs. Different decomposition levels from 2 to 5 were considered for wavelet packets with application of Haar, rbio3.1, sym7, dmey wavelets. A one second windowing was introduced for the data segmentation and Shannon's log energy entropy was estimated. Then the statistical non-parametric Wilcoxon model was employed. The result shows that the application of wavelet packet log energy entropy found to be a potential indicator for discriminating epileptic seizure from normal.

Motor Imagery Classification Using EEG Signals for Brain-Computer Interface Applications

2019 ◽  
pp. 241-251
Author(s):  
Subrota Mazumdar ◽  
Rohit Chaudhary ◽  
Suruchi Suruchi ◽  
Suman Mohanty ◽  
Divya Kumari ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Nearest Neighbor ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Test Cases ◽  
Multiple Channels ◽  
Eeg Signals ◽  
Brain Computer Interfacing ◽  
Electroencephalogram Eeg

In this chapter, a nearest neighbor (k-NN)-based method for efficient classification of motor imagery using EEG for brain-computer interfacing (BCI) applications has been proposed. Electroencephalogram (EEG) signals are obtained from multiple channels from brain. These EEG signals are taken as input features and given to the k-NN-based classifier to classify motor imagery. More specifically, the chapter gives an outline of the Berlin brain-computer interface that can be operated with minimal subject change. All the design and simulation works are carried out with MATLAB software. k-NN-based classifier is trained with data from continuous signals of EEG channels. After the network is trained, it is tested with various test cases. Performance of the network is checked in terms of percentage accuracy, which is found to be 99.25%. The result suggested that the proposed method is accurate for BCI applications.

Automatic selection of frequency and time intervals for classification of EEG signals

2007 ◽  
Vol 43 (25) ◽  
pp. 1406 ◽  
Author(s):  
M. Dalponte ◽  
F. Bovolo ◽  
L. Bruzzone
Keyword(s):  
Eeg Signals ◽  
Time Intervals ◽  
Automatic Selection ◽  
Selection Of
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