Study of motor imagery for multiclass brain system interface with a special focus in the same limb movement

Patil, Mohit; Garg, Nikhil; Kanungo, Lizy; Baths, Veeky

doi:10.1109/iccicc46617.2019.9146105

Cited by 3 publications

(4 citation statements)

References 39 publications

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Section: Introductionmentioning

confidence: 99%

“…In an attempt to classify emotions using EEG signals, many time-domain, frequency-domain, continuity, complexity (Gao et al, 2019 ; Galvão et al, 2021 ), statistical, microstate (Lehmann, 1990 ; Milz et al, 2016 ; Shen X. et al, 2020 ), wavelet-based (Jie et al, 2014 ), and Empirical (Patil et al, 2019 ; Subasi et al, 2021 ) features extraction techniques have been proposed. We have summarized the latest studies using EEG to recognize the emotional state in Table 1 .…”

Section: Introductionmentioning

confidence: 99%

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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: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Garg

Anand

et al. 2022

Front. Hum. Neurosci.

Self Cite

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“…In an attempt to classify emotions using EEG signals, many time-domain, frequency-domain, continuity, complexity [41], [42], statistical, microstate [43]- [45], wavelet-based [46]. Empirical features [47], [48] have been used to aid better classification results using advanced ensemble learning techniques [49] or using deep networks, often referred to as bag of deep features [50]. We have summarized the latest studies using EEG to recognize the emotional state in Table I This paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

“…In an attempt to classify emotions using EEG signals, many time-domain, frequency-domain, continuity, complexity(Gao et al, 2019; Galvão et al, 2021), statistical, microstate (Milz et al, 2016; Lehmann, 1990; Shen et al, 2020b), wavelet-based (Jie et al, 2014). Empirical features (Subasi et al, 2021; Patil et al, 2019) have been used to aid better classification results using advanced ensemble learning techniques (Fang et al, 2021) or using deep networks, often referred to as bag of deep features (Asghar et al, 2019). We have summarized the latest studies using EEG to recognize the emotional state in Table ??…”

Section: Introductionmentioning

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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