Motor Imagery Based Multimodal Biometric User Authentication System Using EEG

Valsaraj, Akshay; Madala, Ithihas; Garg, Nikhil; Patil, Mohit; Baths, Veeky

doi:10.1109/cw49994.2020.00050

Cited by 12 publications

(9 citation statements)

References 24 publications

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“…Sub-band Information Quantity (SIQ) refers to the entropy of the decomposed EEG wavelet signal for each of the five frequency bands (Jia et al, 2008 ; Valsaraj et al, 2020 ). In our analysis, the EEG signal was decomposed using a butter-worth filter of order 7, followed by an FIR/IIR filter.…”

Section: Methodsmentioning

confidence: 99%

Decoding the neural signatures of valence and arousal from portable EEG headset

Garg

Anand

et al. 2022

Front. Hum. Neurosci.

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Emotion classification using electroencephalography (EEG) data and machine learning techniques have been on the rise in the recent past. However, past studies use data from medical-grade EEG setups with long set-up times and environment constraints. This paper focuses on classifying emotions on the valence-arousal plane using various feature extraction, feature selection, and machine learning techniques. We evaluate different feature extraction and selection techniques and propose the optimal set of features and electrodes for emotion recognition. The images from the OASIS image dataset were used to elicit valence and arousal emotions, and the EEG data was recorded using the Emotiv Epoc X mobile EEG headset. The analysis is carried out on publicly available datasets: DEAP and DREAMER for benchmarking. We propose a novel feature ranking technique and incremental learning approach to analyze performance dependence on the number of participants. Leave-one-subject-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. The collected dataset and pipeline are also published. Our study achieved a root mean square score (RMSE) of 0.905 on DREAMER, 1.902 on DEAP, and 2.728 on our dataset for valence label and a score of 0.749 on DREAMER, 1.769 on DEAP, and 2.3 on our proposed dataset for arousal label.

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Section: Methodsmentioning

confidence: 99%

Decoding the neural signatures of valence and arousal from portable EEG headset

Garg

Anand

et al. 2022

Front. Hum. Neurosci.

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“…References EEG Recording Real [23], [32], [24], [43], [33], [25], [26], [34], [35], [37], [38], [40], [41], [44], [28], [45], [29], [42], [30] Motor imagery [50], [51], [46], [52], [47], [48] Real and motor imagery [60], [56], [57], [58] Rest state [61] a large dataset. However, it requires a long training time [21].…”

Section: Activities Duringmentioning

confidence: 99%

“…Algorithms SVM [50], [24], [56], [26], [46], [58], [44], [47], [28], [45], [29], [30] Random forest [23], [58], [29] Bayesian network [56] Naive Bayes [61], [25], [58] KNN [57], [43], [58], [ [50], [32], [60], [43], [33], [44], [45] CNN [51], [34], [35], [52], [37], [38], [48] LSTM [51], [38], [40], [41] RNN [38] Using these functions, a supervised ECOC was eventually trained using an SVM classifier to classify individuals with EEG testing signals. The real positive rating of 94.44% for the suggested procedure indicated a tentative trial of nine EEG records from nine participants.…”

Section: Machine Learning Referencesmentioning

confidence: 99%

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EEG-based Biometric Authentication Using Machine Learning: A Comprehensive Survey

et al. 2022

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An electroencephalogram (EEG) is a measurement that reflects the overall electrical activity in the brain. EEG signals are effective for biometric authentication and robust against malware attacks and any kind of fraud activities due to the uniqueness of the signals. Significant progress in research on EEG-based authentication has been achieved in the last few years, with machine learning being extensively used for classifying EEG signals. However, to the best of our knowledge, there has been no investigation into the overall progress made in such research. In this paper, the literature on the various factors involved in state-of-the-art biometric authentication systems is reviewed. We provide a thorough comparison of different machine learning biometric authentication techniques. The comparison criteria include the research objectives, machine learning algorithms, computational complexity, source of brainwaves, feature extraction methods, number of channels, and so on. Alongside the discussion of existing works, directions for future research are suggested to improve authentication accuracy. This paper provides an in-depth discussion of different advanced biometric authentication techniques, and a vivid picture of state-of-the-art machine learning-based biometric authentication techniques using EEG.

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“…(Phung et al, 2014) 4.1.2 Subband Information Quantity Sub-band Information Quantity (SIQ) refers to the entropy of the decomposed EEG wavelet signal for each of the five frequency bands. (Jia et al, 2008;Valsaraj et al, 2020). In our analysis, the EEG signal was decomposed using a butter-worth filter of order 7 followed by an FIR/IIR filter.…”

Section: Shannon Entropymentioning

confidence: 99%

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

Garg

Parrivesh

et al. 2021

Preprint

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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.

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Motor Imagery Based Multimodal Biometric User Authentication System Using EEG

Cited by 12 publications

References 24 publications

Decoding the neural signatures of valence and arousal from portable EEG headset

Decoding the neural signatures of valence and arousal from portable EEG headset

EEG-based Biometric Authentication Using Machine Learning: A Comprehensive Survey

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

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