scholarly journals BioPyC, an Open-Source Python Toolbox for Offline Electroencephalographic and Physiological Signals Classification

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5740
Author(s):  
Aurélien Appriou ◽  
Léa Pillette ◽  
David Trocellier ◽  
Dan Dutartre ◽  
Andrzej Cichocki ◽  
...  
Keyword(s):  
Open Source ◽  
Electrodermal Activity ◽  
Statistical Tests ◽  
Eeg Signals ◽  
Data Formats ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Biosignal Processing ◽  
Free Open Source

Research on brain–computer interfaces (BCIs) has become more democratic in recent decades, and experiments using electroencephalography (EEG)-based BCIs has dramatically increased. The variety of protocol designs and the growing interest in physiological computing require parallel improvements in processing and classification of both EEG signals and bio signals, such as electrodermal activity (EDA), heart rate (HR) or breathing. If some EEG-based analysis tools are already available for online BCIs with a number of online BCI platforms (e.g., BCI2000 or OpenViBE), it remains crucial to perform offline analyses in order to design, select, tune, validate and test algorithms before using them online. Moreover, studying and comparing those algorithms usually requires expertise in programming, signal processing and machine learning, whereas numerous BCI researchers come from other backgrounds with limited or no training in such skills. Finally, existing BCI toolboxes are focused on EEG and other brain signals but usually do not include processing tools for other bio signals. Therefore, in this paper, we describe BioPyC, a free, open-source and easy-to-use Python platform for offline EEG and biosignal processing and classification. Based on an intuitive and well-guided graphical interface, four main modules allow the user to follow the standard steps of the BCI process without any programming skills: (1) reading different neurophysiological signal data formats, (2) filtering and representing EEG and bio signals, (3) classifying them, and (4) visualizing and performing statistical tests on the results. We illustrate BioPyC use on four studies, namely classifying mental tasks, the cognitive workload, emotions and attention states from EEG signals.

Classification of productive profile using brain signals: Deep learning and neuroscience meets the mining industry

2021 ◽  
Author(s):  
Ana Siravenha ◽  
Walisson Gomes ◽  
Renan Tourinho ◽  
Sergio Viademonte ◽  
Bruno Gomes
Keyword(s):  
Deep Learning ◽  
Iron Ore ◽  
Signal To Noise Ratio ◽  
Mining Industry ◽  
Eeg Signals ◽  
Neural Data ◽  
Technical Staff ◽  
Brain Signals ◽  
Mining Complex

Classification of electroencephalography (EEG) signals is a complex task. EEG is a non-stationary time process with low signal to noise ratio. Among many methods usedfor EEG classification, those based on Deep Learning (DL) have been relatively successful in providing high classification accuracies. In the present study we aimed at classify resting state EEGs measured from workers of a mining complex. Just after the EEG has been collected, the workers undergonetraining in a 4D virtual reality simulator that emulates the iron ore excavation from which parameters related to their performance were analyzed by the technical staff who classified the workers into four groups based on their productivity. Twoconvolutional neural networks (ConvNets) were then used to classify the workers EEG bases on the same productivity label provided by the technical staff. The neural data was used in three configurations in order to evaluate the amount of datarequired for a high accuracy classification. Isolated, the channel T5 achieved 83% of accuracy, the subtraction of channels P3 and Pz achieved 99% and using all channels simultaneously was 99.40% assertive. This study provides results that add to the recent literature showing that even simple DL architectures are able to handle complex time series such as the EEG. In addition, it pin points an application in industry with vast possibilities of expansion.

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 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.

Automatic channel selection in EEG signals for classification of left or right hand movement in Brain Computer Interfaces using improved binary gravitation search algorithm

2017 ◽  
Vol 33 ◽  
pp. 109-118 ◽  
Author(s):  
Alireza Ghaemi ◽  
Esmat Rashedi ◽  
Ali Mohammad Pourrahimi ◽  
Mehdi Kamandar ◽  
Farhad Rahdari
Keyword(s):  
Search Algorithm ◽  
Hand Movement ◽  
Channel Selection ◽  
Brain Computer Interfaces ◽  
Eeg Signals ◽  
Computer Interfaces ◽  
Right Hand

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.

Development of emotion classifier based on absolute and differential attributes of averaged signals of visually stimulated event related potentials

2020 ◽  
Vol 42 (11) ◽  
pp. 2057-2067
Author(s):  
Moon Inder Singh ◽  
Mandeep Singh
Keyword(s):  
Event Related Potentials ◽  
Central Line ◽  
Support Vector ◽  
Eeg Signals ◽  
High Arousal ◽  
Brain Signals ◽  
Polynomial Classifier ◽  
Related Potentials ◽  
Daunting Challenge

Analysis and study of abstract human relations have always posed a daunting challenge for technocrats engaged in the field of psychometric analysis. The study on emotion recognition is all the more demanding as it involves integration of abstract phenomenon of emotion causation and emotion appraisal through physiological and brain signals. This paper describes the classification of human emotions into four classes, namely: low valence high arousal (LVHA), high valence high arousal (HVHA), high valence low arousal (HVLA) and low valence low arousal (LVLA) using Electroencephalogram (EEG) signals. The EEG signals have been collected on three EEG electrodes along the central line viz: Fz, Cz and Pz. The analysis has been done on average event related potentials (ERPs) and difference of average ERPs using Support Vector Machine (SVM) polynomial classifier. The four-class classification accuracy of 75% using average ERP attributes and an accuracy of 76.8% using difference of ERPs as attributes has been obtained. The accuracy obtained using differential average ERP attributes is better as compared with the already existing studies.

scholarly journals Open Software/Hardware Platform for Human-Computer Interface Based on Electrooculography (EOG) Signal Classification

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2443
Author(s):  
Jayro Martínez-Cerveró ◽  
Majid Khalili Ardali ◽  
Andres Jaramillo-Gonzalez ◽  
Shizhe Wu ◽  
Alessandro Tonin ◽  
...  
Keyword(s):  
Open Source ◽  
Raspberry Pi ◽  
Support Vector ◽  
Svm Classifier ◽  
Computer Interfaces ◽  
Human Computer Interfaces ◽  
Online Classification ◽  
Acquisition Device ◽  
Maximum Minimum

Electrooculography (EOG) signals have been widely used in Human-Computer Interfaces (HCI). The HCI systems proposed in the literature make use of self-designed or closed environments, which restrict the number of potential users and applications. Here, we present a system for classifying four directions of eye movements employing EOG signals. The system is based on open source ecosystems, the Raspberry Pi single-board computer, the OpenBCI biosignal acquisition device, and an open-source python library. The designed system provides a cheap, compact, and easy to carry system that can be replicated or modified. We used Maximum, Minimum, and Median trial values as features to create a Support Vector Machine (SVM) classifier. A mean of 90% accuracy was obtained from 7 out of 10 subjects for online classification of Up, Down, Left, and Right movements. This classification system can be used as an input for an HCI, i.e., for assisted communication in paralyzed people.

scholarly journals Prediction of Hand Movement Speed and Force from Single-trial EEG with Convolutional Neural Networks

2018 ◽  
Author(s):  
Ramiro Gatti ◽  
Yanina Atum ◽  
Luciano Schiaffino ◽  
Mads Jochumsen ◽  
José Biurrun Manresa
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Hand Movement ◽  
Substantial Improvement ◽  
Movement Speed ◽  
Single Trial ◽  
Eeg Signals ◽  
Brain Signals ◽  
Electroencephalogram Eeg

AbstractBuilding accurate movement decoding models from brain signals is crucial for many biomedical applications. Decoding specific movement features, such as speed and force, may provide additional useful information at the expense of increasing the complexity of the decoding problem. Recent attempts to predict movement speed and force from the electroencephalogram (EEG) achieved classification accuracy levels not better than chance, stressing the demand for more accurate prediction strategies. Thus, the aim of this study was to improve the prediction accuracy of hand movement speed and force from single-trial EEG signals recorded from healthy volunteers. A strategy based on convolutional neural networks (ConvNets) was tested, since it has previously shown good performance in the classification of EEG signals. ConvNets achieved an overall accuracy of 84% in the classification of two different levels of speed and force (4-class classification) from single-trial EEG. These results represent a substantial improvement over previously reported results, suggesting that hand movement speed and force can be accurately predicted from single-trial EEG.

scholarly journals An Evaluation of Different Fast Fourier Transform - Transfer Learning Pipelines for the Classification of Wink-based EEG Signals

Mekatronika ◽  
2020 ◽  
Vol 2 (1) ◽  
pp. 1-7
Author(s):  
Jothi Letchumy Mahendra Kumar ◽  
Mamunur Rashid ◽  
Rabiu Muazu Musa ◽  
Mohd Azraai Mohd Razman ◽  
Norizam Sulaiman ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Transfer Learning ◽  
Test Accuracy ◽  
Eeg Signals ◽  
Computer Interfaces ◽  
Physical Limitations ◽  
Search Approach ◽  
Electroencephalogram Eeg

Brain Computer-Interfaces (BCI) offers a means of controlling prostheses for neurological disorder patients, primarily owing to their inability to control such devices due to their inherent physical limitations. More often than not, the control of such devices exploits the use of Electroencephalogram (EEG) signals. Nonetheless, it is worth noting that the extraction of the features is often a laborious undertaking. The use of Transfer Learning (TL) has been demonstrated to be able to mitigate the issue. However, the employment of such a method towards BCI applications, particularly with regards to EEG signals are limited. The present study aims to assess the effectiveness of a number of DenseNet TL models, viz. DenseNet169, DenseNet121 and DenseNet201 in extracting features for the classification of wink-based EEG signals. The extracted features are then classified through an optimised Random Forest (RF) classifier. The raw EEG signals are transformed into a spectrogram image via Fast Fourier Transform (FFT) before it was fed into selected TL models. The dataset was split with a stratified ratio of 60:20:20 into train, test, and validation datasets, respectively. The hyperparameters of the RF model was optimised through the grid search approach that utilises the five-fold cross-validation technique. It was established from the study that amongst the DenseNet pipelines evaluated, the DenseNet169 performed the best with an overall validation and test accuracy of 89%. The findings of the present investigation could facilitate BCI applications, e.g., for a grasping exoskeleton.

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