ArticleFrontiers in human neuroscience2018
Emotion-Related Consciousness Detection in Patients With Disorders of Consciousness Through an EEG-Based BCI System.
Article in Frontiers in human neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 3 of them syntheses that pooled it.
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Who cites it
27 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Past, Present, and Future of EEG-Based BCI Applications.Sensors (Basel, Switzerland) · 2022Pooled it
- EEG-based Brain-Computer Interfaces for people with Disorders of Consciousness: Features and applications. A systematic review.Frontiers in human neuroscience · 2022Pooled it
- Managing disorders of consciousness: the role of electroencephalography.Journal of neurology · 2021Pooled it
- Emotion recognition using spectral-spatial attention multi-temporal scale network: EEG study.Brain informatics · 2026Article
- Emotional recognition technologies applied to health: review and challenges.Revista brasileira de psiquiatria (Sao Paulo, Brazil : 1999) · 2025Review
- A motor imagery classification model based on hybrid brain-computer interface and multitask learning of electroencephalographic and electromyographic deep features.Frontiers in physiology · 2024Article
- A novel feature fusion network for multimodal emotion recognition from EEG and eye movement signals.Frontiers in neuroscience · 2023Article
- Recent Progress in Wearable Brain-Computer Interface (BCI) Devices Based on Electroencephalogram (EEG) for Medical Applications: A Review.Health data science · 2023Review
- Fusion of EEG-Based Activation, Spatial, and Connection Patterns for Fear Emotion Recognition.Computational intelligence and neuroscience · 2022Article
- How brain-computer interface technology may improve the diagnosis of the disorders of consciousness: A comparative study.Frontiers in neuroscience · 2022Article
- Multi-Modal Integration of EEG-fNIRS for Characterization of Brain Activity Evoked by Preferred Music.Frontiers in neurorobotics · 2022Article
- Residual Cognitive Capacities in Patients With Cognitive Motor Dissociation, and Their Implications for Well-Being.The Journal of medicine and philosophy · 2021Article
- Positive effects of music therapist's selected auditory stimulation on the autonomic nervous system of patients with disorder of consciousness: a randomized controlled trial.Neural regeneration research · 2021Article
- Conditional Entropy: A Potential Digital Marker for Stress.Entropy (Basel, Switzerland) · 2021Article
- P3-MSDA: Multi-Source Domain Adaptation Network for Dynamic Visual Target Detection.Frontiers in human neuroscience · 2021Article
- TechnoBrainBodies-in-Cultures: An Intersectional Case.Frontiers in sociology · 2021Article
- Research on the Emotions Based on BrainFrontiers in psychology · 2021Review
- EEG-Based Emotion Classification for Alzheimer's Disease Patients Using Conventional Machine Learning and Recurrent Neural Network Models.Sensors (Basel, Switzerland) · 2020Article
- Lightweight Building of an Electroencephalogram-Based Emotion Detection System.Brain sciences · 2020Article
- EEG dynamics and neural generators of psychological flow during one tightrope performance.Scientific reports · 2020Observational
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7 authors.
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Abstract
For patients with disorders of consciousness (DOC), such as vegetative state (VS) and minimally conscious state (MCS), detecting and assessing the residual cognitive functions of the brain remain challenging. Emotion-related cognitive functions are difficult to detect in patients with DOC using motor response-based clinical assessment scales such as the Coma Recovery Scale-Revised (CRS-R) because DOC patients have motor impairments and are unable to provide sufficient motor responses for emotion-related communication. In this study, we proposed an EEG-based brain-computer interface (BCI) system for emotion recognition in patients with DOC. Eight patients with DOC (5 VS and 3 MCS) and eight healthy controls participated in the BCI-based experiment. During the experiment, two movie clips flashed (appearing and disappearing) eight times with a random interstimulus interval between flashes to evoke P300 potentials. The subjects were instructed to focus on the crying or laughing movie clip and to count the flashes of the corresponding movie clip cued by instruction. The BCI system performed online P300 detection to determine which movie clip the patients responsed to and presented the result as feedback. Three of the eight patients and all eight healthy controls achieved online accuracies based on P300 detection that were significantly greater than chance level. P300 potentials were observed in the EEG signals from the three patients. These results indicated the three patients had abilities of emotion recognition and command following. Through spectral analysis, common spatial pattern (CSP) and differential entropy (DE) features in the delta, theta, alpha, beta, and gamma frequency bands were employed to classify the EEG signals during the crying and laughing movie clips. Two patients and all eight healthy controls achieved offline accuracies significantly greater than chance levels in the spectral analysis. Furthermore, stable topographic distribution patterns of CSP and DE features were observed in both the healthy subjects and these two patients. Our results suggest that cognitive experiments may be conducted using BCI systems in patients with DOC despite the inability of such patients to provide sufficient behavioral responses.
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