Hyperscanning EEG and Classification Based on Riemannian Geometry for Festive and Violent Mental State Discrimination

Cebolla, Ana Maria; Chartier, Gaëlle; Petieau, Mathieu; Bontempi, Gianluca; Berthoz, Alain; Chéron, Guy

doi:10.3389/fnins.2020.588357

Cited by 10 publications

(7 citation statements)

References 64 publications

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“…For instance, Support Vector Machines have been used in a previous HS study to characterize the contributions of inter-brain and intra-brain connectivity measures in classifying dyadic interactions compared to individual interactions with computers during a visuomotor task [ 45 ]. Another study used a classifier based on Riemannian geometry to discriminate between different emotional states of interacting partners [ 46 ].…”

Section: Eeg Hyperscanningmentioning

confidence: 99%

EEG hyperscanning in motor rehabilitation: a position paper

Short

Hernandez-Pavon

Jones

et al. 2021

J NeuroEngineering Rehabil

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Studying the human brain during interpersonal interaction allows us to answer many questions related to motor control and cognition. For instance, what happens in the brain when two people walking side by side begin to change their gait and match cadences? Adapted from the neuroimaging techniques used in single-brain measurements, hyperscanning (HS) is a technique used to measure brain activity from two or more individuals simultaneously. Thus far, HS has primarily focused on healthy participants during social interactions in order to characterize inter-brain dynamics. Here, we advocate for expanding the use of this electroencephalography hyperscanning (EEG-HS) technique to rehabilitation paradigms in individuals with neurological diagnoses, namely stroke, spinal cord injury (SCI), Parkinson’s disease (PD), and traumatic brain injury (TBI). We claim that EEG-HS in patient populations with impaired motor function is particularly relevant and could provide additional insight on neural dynamics, optimizing rehabilitation strategies for each individual patient. In addition, we discuss future technologies related to EEG-HS that could be developed for use in the clinic as well as technical limitations to be considered in these proposed settings.

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Section: Eeg Hyperscanningmentioning

confidence: 99%

EEG hyperscanning in motor rehabilitation: a position paper

Short

Hernandez-Pavon

Jones

et al. 2021

J NeuroEngineering Rehabil

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“…Additionally, spatial filtering through optimizing the covariance matrices with training data using common spatial patterns (CSP) and Riemannian geometry (Barachant et al, 2011 ) have been used to aid better classification results (Simar et al, 2020 ). However, such methods are only applicable for classification tasks, and extension to regression problems is not in the scope of this study.…”

Section: Discussionmentioning

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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“…Paris-Saclay), Pierre Clisson (Timeflux Research Group), Quentin Barthélemy (Foxstream) Riemannian Geometry (RG) is a subject of growing interest within the BCI community. Machine learning methods based on RG have demonstrated robustness, accuracy, and transfer learning capabilities for the classification of motor imagery [64], ERPs [65], SSVEPs [66], sleep stages [67], and other mental states [68]. This workshop provided an overview of RG, demonstrating its practical use for signal pre-processing, data analysis, mental state classification, and regression.…”

Section: Riemannian Geometry Methods For Eeg Preprocessing Analysis A...mentioning

confidence: 99%

Workshops of the eighth international brain–computer interface meeting: BCIs: the next frontier

Huggins

Krusienski

Vansteensel

et al. 2022

Brain-Computer Interfaces

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The Eighth International Brain-Computer Interface (BCI) Meeting was held June 7-9, 2021 in a virtual format. The conference continued the BCI Meeting series' interactive nature with 21 workshops covering the breadth of topics in BCI (also called brain-machine interface) research. Some workshops provided detailed examinations of methods, hardware, or processes. Others focused on BCI applications or user groups. Several workshops continued consensus building efforts designed to create BCI standards and improve comparisons between studies and the potential for meta-analysis and large multi-site clinical trials. Ethical and translational considerations were the primary topic for some workshops or an important secondary consideration for others. The range of BCI applications continues to expand, with more workshops focusing on approaches that can extend beyond the needs of those with physical impairments. This paper summarizes each workshop, provides background information and references for further study, summarizes discussions, and describes the resulting conclusion, challenges, or initiatives.

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Hyperscanning EEG and Classification Based on Riemannian Geometry for Festive and Violent Mental State Discrimination

Cited by 10 publications

References 64 publications

EEG hyperscanning in motor rehabilitation: a position paper

EEG hyperscanning in motor rehabilitation: a position paper

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

Workshops of the eighth international brain–computer interface meeting: BCIs: the next frontier

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