Online Learning for Wearable EEG-Based Emotion Classification

Moontaha, Sidratul; Schumann, Franziska Elisabeth Friederike; Arnrich, Bert

doi:10.3390/s23052387

Cited by 13 publications

(6 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These bioelectrical signals, amplified and digitized by Muse, are then translated into data accessible through a dedicated app, offering a real-time glimpse into the brain's inner workings. Recent peer-reviewed articles demonstrated the validity of data obtained by Muse in the context of neurosciences (Krigolson et al, 2017;LaRocco et al, 2020;Moontaha et al, 2023).…”

Section: Validating Transdermal Delivery Of Gaba By Eeg: the Citizen ...mentioning

confidence: 90%

Effects of a Topical Composition of GABA and Microbial Chondroitin Sulfate on Mental Calmness and Brain Function

Ruggiero

2024

American Journal of Immunology

View full text Add to dashboard Cite

This article describes a novel topical composition constituted by Gamma-Aminobutyric Acid (GABA) and microbial chondroitin sulfate. This composition, formulated by the author and manufactured in a certified facility in the United States of America, exhibits potential for non-invasive intervention in anxiety management, sleep quality enhancement and promotion of brain-related functions like meditation and nonlocal consciousness. The core component comprises GABA, a key inhibitory neurotransmitter within the central nervous system, complexed with a suitable carrier molecule, microbial chondroitin sulfate. This complexation aims to enhance transdermal absorption and optimize the delivery of GABA to target tissues. The proposed mechanism of action involves modulation of GABAergic signal transduction pathways. Application of the topical composition allows GABA penetration into the skin and potentially deeper tissues, where it interacts with GABA receptors. This interaction leads to mental calmness and relaxation as assessed by electroencephalography. The influence of GABAergic signaling on brain circuits demonstrated in this article, when associated with meditation and nonlocal consciousness, suggests potential for the composition to support these practices. The advantage of such an approach consists in avoiding the potential systemic side effects associated with oral or injectable administration of GABA, anxiolytics, or hypnotics. Furthermore, the combination of GABA and microbial chondroitin sulfate may offer synergistic benefits, potentially enhancing the overall biological effects. In conclusion, this article presents a novel topical approach for managing anxiety, enhancing sleep quality and potentially facilitating meditation and nonlocal consciousness through the modulation of GABAergic signaling pathways. While further research is necessary, the potential for a non-invasive and potentially side-effect-free intervention in these areas is promising.

show abstract

Section: Validating Transdermal Delivery Of Gaba By Eeg: the Citizen ...mentioning

confidence: 90%

Effects of a Topical Composition of GABA and Microbial Chondroitin Sulfate on Mental Calmness and Brain Function

Ruggiero

2024

American Journal of Immunology

View full text Add to dashboard Cite

show abstract

“…One of these methods, Ref. [32], focuses on a real-time method, which employs online learning techniques, including adaptive random forest, streaming random patches and logistic regression. Ref.…”

Section: Deep Learning Approachmentioning

confidence: 99%

EEG Emotion Classification Based on Graph Convolutional Network

Fan,

Chen,

Xia

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

EEG-based emotion recognition is a task that uses scalp-EEG data to classify the emotion states of humans. The study of EEG-based emotion recognition can contribute to a large spectrum of application fields including healthcare and human–computer interaction. Recent studies in neuroscience reveal that the brain regions and their interactions play an essential role in the processing of different stimuli and the generation of corresponding emotional states. Nevertheless, such regional interactions, which have been proven to be critical in recognizing emotions in neuroscience, are largely overlooked in existing machine learning or deep learning models, which focus on individual channels in brain signals. Motivated by this, in this paper, we present RGNet, a model that is designed to learn the regional level representation of EEG signal for accurate emotion recognition. Specifically, after applying preprocessing and feature extraction techniques on raw signals, RGNet adopts a novel region-wise encoder to extract the features of channels located within each region as input to compute the regional level features, enabling the model to effectively explore the regional functionality. A graph is then constructed by considering each region as a node and connections between regions as edges, upon which a graph convolutional network is designed with spectral filtering and learned adjacency matrix. Instead of focusing on only the spatial proximity, it allows the model to capture more complex functional relationships. We conducted experiments from the perspective of region division strategies, region encoders and input feature types. Our model has achieved 98.64% and 99.33% for Deap and Dreamer datasets, respectively. The comparison studies show that RGNet outperforms the majority of the existing models for emotion recognition from EEG signals.

show abstract

“…Moreover, newer systems often make use of dry electrode designs, which eliminate the need for conductive gels that were previously required for proper functioning [43][44][45][46][47]. Furthermore, many new portable devices exist now that allow users to easily record their own EEG signals without having to visit a clinic [9,[48][49][50][51]. These devices typically employ Bluetooth connectivity so that they can transmit their data wirelessly directly to computers for analysis.…”

Section: Eeg Platformmentioning

confidence: 99%

State-of-the-Art on Brain-Computer Interface Technology

Pekša

Mamchur

2023

Sensors

View full text Add to dashboard Cite

This paper provides a comprehensive overview of the state-of-the-art in brain–computer interfaces (BCI). It begins by providing an introduction to BCIs, describing their main operation principles and most widely used platforms. The paper then examines the various components of a BCI system, such as hardware, software, and signal processing algorithms. Finally, it looks at current trends in research related to BCI use for medical, educational, and other purposes, as well as potential future applications of this technology. The paper concludes by highlighting some key challenges that still need to be addressed before widespread adoption can occur. By presenting an up-to-date assessment of the state-of-the-art in BCI technology, this paper will provide valuable insight into where this field is heading in terms of progress and innovation.

show abstract

Online Learning for Wearable EEG-Based Emotion Classification

Cited by 13 publications

References 67 publications

Effects of a Topical Composition of GABA and Microbial Chondroitin Sulfate on Mental Calmness and Brain Function

Effects of a Topical Composition of GABA and Microbial Chondroitin Sulfate on Mental Calmness and Brain Function

EEG Emotion Classification Based on Graph Convolutional Network

State-of-the-Art on Brain-Computer Interface Technology

Contact Info

Product

Resources

About