scholarly journals Emotion Detection Analysis using EEG and Physiological Signals for Hybrid Systems

2020 ◽  
Vol 9 (6) ◽  
pp. 534-543
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Physiological Signals ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Emotion Detection ◽  
Detection Analysis ◽  
Learning Techniques ◽  
Eeg Data ◽  
Skin Response

Emotions are an inevitable and integral part of human existence. They form the basis of decisions taken by individuals and the way they perceive their surroundings. Method of articulation of emotions have changed with the increment in dependency between people and innovation. Now the need to recognize emotions has increased with the increasing role of human-Computer Interface (HCI) technology. There are many ways to record and identify human’s emotion using different neurophysiological measurements/ technologies like GSR(Galvanic Skin Response), Electromyography (EMG), Electrocardiogram (ECG) and Electroencephalography (EEG). In this paper, the focus is on emotion detection using EEG signals and other physiological signals and further analyzing them. There exist various machine learning techniques that have been used to pre-process and classify EEG data, have been reviewed in the paper. The analysis involves major aspects of the emotion recognition process like feature extraction, classification and comparison of the approaches. Different supervised machine learning algorithms have been applied to classify the EEG data. This paper focuses on comprehensive analysis of existing systems and based on the result propose the techniques which when applied will reap high-quality results.

scholarly journals Reliable and automatic epilepsy classification with affordable, consumer-grade electroencephalography in rural sub-Saharan Africa

2018 ◽  
Author(s):  
Vincent T. van Hees ◽  
Eric van Diessen ◽  
Michel R.T. Sinke ◽  
Jan W. Buitenhuis ◽  
Frank van der Maas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Low Income ◽  
Low Cost ◽  
Sub Saharan Africa ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Guinea Bissau ◽  
Learning Techniques ◽  
Eeg Data ◽  
Unseen Data

AbstractEpilepsy is largely under-diagnosed in low-income and middle-income countries, due to lack of medical specialists and expensive electroencephalography (EEG) hardware. In this study we investigate if low-cost consumer-grade EEG in combination with machine learning techniques can offer a reliable screening tool to improve diagnosis rates.We acquired brain signals in people with epilepsy (N=163) and healthy controls (N=138) in two difficult-to-reach areas in rural Guinea-Bissau and Nigeria. Five minutes of fourteen channel resting-state EEG data were acquired with a portable, low-cost consumer-grade EEG recording headset. EEG channel time-series were divided in four-second artifact-free epochs and transformed into delta, theta, alpha, beta and gamma wavelet frequencies. Summary measures such as the mean, standard deviation, minimal value and maximal value of the epoch signal fluctuations were used to train a random forest classifier. Epilepsy diagnosis based on at least three months seizure calendar data was used as the gold standard diagnosis. To prevent too optimistic classification the trained model was evaluated with EEG data from subjects not used in the training. In addition, we tested a classification model trained on Nigeria data against data from people in Guinea-Bissau and vice versa. The most contributing data features in the EEG were found in the beta and theta frequencies in Guinea-Bissau and Nigeria, respectively. Within-country model performance was good with area under the receiver-operating curves of 0.85 and 0.78 (± 0.02 standard errors) in unseen data in Guinea-Bissau and Nigeria, respectively. Across-country performance was moderate (0.62 and 0.64 ± 0.02).Our data suggests that a combination of low cost electroencephalography and machine learning techniques may facilitate diagnostic screening for epilepsy in the most remote areas of the world.

scholarly journals Decoding the Neural Signatures of Valence and Arousal From Portable EEG Headset

2021 ◽  
Author(s):  
Nikhil Garg ◽  
Rohit Garg ◽  
Parrivesh NS ◽  
Apoorv Anand ◽  
V.A.S. Abhinav ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Feature Selection ◽  
Emotion Recognition ◽  
Machine Learning Techniques ◽  
Emotion Classification ◽  
Learning Techniques ◽  
Eeg Data ◽  
Valence And Arousal ◽  
Mean Square Errors

This paper focuses on classifying emotions on the valence-arousal plane using various feature extraction, feature selection and machine learning techniques. Emotion classification using EEG data and machine learning techniques has been on the rise in the recent past. We evaluate different feature extraction techniques, feature selection techniques and propose the optimal set of features and electrodes for emotion recognition. The images from the OASIS image dataset were used for eliciting the Valence and Arousal emotions, and the EEG data was recorded using the Emotiv Epoc X mobile EEG headset. The analysis is additionally carried out on publicly available datasets: DEAP and DREAMER. We propose a novel feature ranking technique and incremental learning approach to analyze the dependence of performance on the number of participants. Leave one out cross-validation was carried out to identify subject bias in emotion elicitation patterns. The importance of different electrode locations was calculated, which could be used for designing a headset for emotion recognition. Our study achieved root mean square errors of less than 0.75 on DREAMER, 1.76 on DEAP, and 2.39 on our dataset.

scholarly journals Optimizing Distributed Machine Learning for Large Scale EEG Data Set

2017 ◽  
Vol 1 (1) ◽  
pp. 114
Author(s):  
M Bilal Shaikh ◽  
M Abdul Rehman ◽  
Attaullah Sahito
Keyword(s):  
Machine Learning ◽  
Data Distribution ◽  
Data Partitioning ◽  
Machine Learning Techniques ◽  
Random Data ◽  
Data Set ◽  
P300 Speller ◽  
Learning Techniques ◽  
Eeg Data ◽  
Distributed Machine Learning

Distributed Machine Learning (DML) has gained its importance more than ever in this era of Big Data. There are a lot of challenges to scale machine learning techniques on distributed platforms. When it comes to scalability, improving the processor technology for high level computation of data is at its limit, however increasing machine nodes and distributing data along with computation looks as a viable solution. Different frameworks   and platforms are available to solve DML problems. These platforms provide automated random data distribution of datasets which miss the power of user defined intelligent data partitioning based on domain knowledge. We have conducted an empirical study which uses an EEG Data Set collected through P300 Speller component of an ERP (Event Related Potential) which is widely used in BCI problems; it helps in translating the intention of subject w h i l e performing any cognitive task. EEG data contains noise due to waves generated by other activities in the brain which contaminates true P300Speller. Use of Machine Learning techniques could help in detecting errors made by P300 Speller. We are solving this classification problem by partitioning data into different chunks and preparing distributed models using Elastic CV Classifier. To present a case of optimizing distributed machine learning, we propose an intelligent user defined data partitioning approach that could impact on the accuracy of distributed machine learners on average. Our results show better average AUC as compared to average AUC obtained after applying random data partitioning which gives no control to user over data partitioning. It improves the average accuracy of distributed learner due to the domain specific intelligent partitioning by the user. Our customized approach achieves 0.66 AUC on individual sessions and 0.75 AUC on mixed sessions, whereas random / uncontrolled data distribution records 0.63 AUC.

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