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 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.

Music Neurotechnology for Sound Synthesis: Sound Synthesis with Spiking Neuronal Networks

Leonardo ◽  
2009 ◽  
Vol 42 (5) ◽  
pp. 439-442 ◽  
Author(s):  
Eduardo R. Miranda ◽  
John Matthias
Keyword(s):  
Cortical Neurons ◽  
Neuronal Networks ◽  
Research Area ◽  
Sound Synthesis ◽  
Ongoing Research ◽  
Computer Interfaces ◽  
Engineering Sciences ◽  
New Research ◽  
Sound Files

Music neurotechnology is a new research area emerging at the crossroads of neurobiology, engineering sciences and music. Examples of ongoing research into this new area include the development of brain-computer interfaces to control music systems and systems for automatic classification of sounds informed by the neurobiology of the human auditory apparatus. The authors introduce neurogranular sampling, a new sound synthesis technique based on spiking neuronal networks (SNN). They have implemented a neurogranular sampler using the SNN model developed by Izhikevich, which reproduces the spiking and bursting behavior of known types of cortical neurons. The neurogranular sampler works by taking short segments (or sound grains) from sound files and triggering them when any of the neurons fire.

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.

Detection of the Intention of Direction Changes During Gait Through EEG Signals

2021 ◽  
pp. 2150015
Author(s):  
Paula Soriano-Segura ◽  
Eduardo Iáñez ◽  
Mario Ortiz ◽  
Vicente Quiles ◽  
José M. Azorín
Keyword(s):  
Neural Plasticity ◽  
Eeg Signals ◽  
Rehabilitation Process ◽  
Computer Interfaces ◽  
Temporal Features ◽  
Average Accuracy ◽  
Technological Tool ◽  
Handicapped People ◽  
Intention To Change ◽  
Selection Of

Brain–Computer Interfaces (BCIs) are becoming an important technological tool for the rehabilitation process of patients with locomotor problems, due to their ability to recover the connection between brain and limbs by promoting neural plasticity. They can be used as assistive devices to improve the mobility of handicapped people. For this reason, current BCIs have to be improved to allow an accurate and natural use of external devices. This work proposes a novel methodology for the detection of the intention to change the direction during gait based on event-related desynchronization (ERD). Frequency and temporal features of the electroencephalographic (EEG) signals are characterized. Then, a selection of the most influential features and electrodes to differentiate the direction change intention from the walking is carried out. Best results are obtained when combining frequency and temporal features with an average accuracy of [Formula: see text]%, which are promising to be applied for future BCIs.

Analysis of the Correlation between Static Visual Stimuli, Eye Movements, and Brain Signals

2021 ◽  
pp. 2150056
Author(s):  
Hamidreza Namazi ◽  
Avinash Menon ◽  
Ondrej Krejcar
Keyword(s):  
Eye Movements ◽  
Visual Stimuli ◽  
Research Area ◽  
The Other ◽  
Important Research ◽  
Eeg Signals ◽  
Brain Signals ◽  
Information Contents ◽  
Important Research Area ◽  
The Brain

Analysis of the correlation among the activities of the eyes and brain is an important research area in physiological science. In this paper, we analyzed the correlation between the reactions of eyes and the brain during rest and while watching different visual stimuli. Since every external stimulus transfers information to the human brain, and on the other hand, eye movements and EEG signals contain information, we utilized Shannon entropy to evaluate the coupling between them. In the experiment, 10 subjects looked at 4 images with different information contents while we recorded their EEG signals and eye movements simultaneously. According to the results, the information contents of eye fluctuations, EEG signals, and visual stimuli are coupled, which reflect the coupling between the brain and eye activities. Similar analyses could be performed to evaluate the correlation among the activities of other organs versus the brain.

An Improved Three-Stage Classifier for Activity Recognition

2017 ◽  
Vol 32 (01) ◽  
pp. 1860003 ◽  
Author(s):  
Enrique Garcia-Ceja ◽  
Ramon F. Brena
Keyword(s):  
Activity Recognition ◽  
Cross Validation ◽  
Inertial Sensors ◽  
Wearable Sensors ◽  
Research Area ◽  
Classification Model ◽  
Homogeneous Groups ◽  
Wide Range ◽  
Important Research Area ◽  
Selection Of

Recently, Human Activity Recognition (HAR) has become an important research area because of its wide range of applications in several domains such as health care, elder care, sports monitoring systems, etc. The use of wearable sensors — specifically the use of inertial sensors such as accelerometers and gyroscopes — has become the most common approach to recognize physical activities because of their unobtrusiveness and ubiquity. Overall, the process of building a HAR system starts with a feature extraction phase and then a classification model is trained. In the work of Siirtola et al. is proposed an intermediate clustering step to find the homogeneous groups of activities. For the recognition step, an instance is assigned to one of the groups and the final classification is performed inside that group. In this work we evaluate the clustering-based approach for activity classification proposed by Siirtola with two additional improvements: automatic selection of the number of groups and an instance reassignment procedure. In the original work, they evaluated their method using decision trees on a sports activities dataset. For our experiments, we evaluated seven different classification models on four public activity recognition datasets. Our results with 10-fold Cross Validation showed that the method proposed by Siirtola with our additional two improvements performed better in the majority of cases as compared to using the single classification model under consideration. When using Leave One User Out Cross Validation (user independent model) we found no differences between the proposed method and the single classification model.

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

scholarly journals Classifying EEG for Brain-Computer Interface: Learning Optimal Filters for Dynamical System Features

2007 ◽  
Vol 2007 ◽  
pp. 1-11 ◽  
Author(s):  
Le Song ◽  
Julien Epps
Keyword(s):  
Dynamical System ◽  
Computer Interface ◽  
Eeg Signals ◽  
Optimal Filters ◽  
Computer Interfaces ◽  
Ratio Criterion ◽  
Fisher Ratio ◽  
Eeg Recordings ◽  
New Framework

Classification of multichannel EEG recordings during motor imagination has been exploited successfully for brain-computer interfaces (BCI). In this paper, we consider EEG signals as the outputs of a networked dynamical system (the cortex), and exploit synchronization features from the dynamical system for classification. Herein, we also propose a new framework for learning optimal filters automatically from the data, by employing a Fisher ratio criterion. Experimental evaluations comparing the proposed dynamical system features with the CSP and the AR features reveal their competitive performance during classification. Results also show the benefits of employing the spatial and the temporal filters optimized using the proposed learning approach.

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