scholarly journals Design of brain-computer interface-based classification model for mining mental state of COVID-19 afflicted mariner’s

2020 ◽  
Vol 71 (4) ◽  
pp. 298-300
Author(s):  
Manik Sharma
Keyword(s):  
Mental State ◽  
Brain Computer Interface ◽  
Classification Model ◽  
Computer Interface

scholarly journals Exploration of User’s Mental State Changes during Performing Brain–Computer Interface

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3169 ◽  
Author(s):  
Li-Wei Ko ◽  
Rupesh Kumar Chikara ◽  
Yi-Chieh Lee ◽  
Wen-Chieh Lin
Keyword(s):  
Working Memory ◽  
Mental State ◽  
Brain Computer Interface ◽  
Physiological State ◽  
Memory Task ◽  
Computer Interface ◽  
Cognitive State ◽  
The Past ◽  
State Changes ◽  
Mental Focus

Substantial developments have been established in the past few years for enhancing the performance of brain–computer interface (BCI) based on steady-state visual evoked potential (SSVEP). The past SSVEP-BCI studies utilized different target frequencies with flashing stimuli in many different applications. However, it is not easy to recognize user’s mental state changes when performing the SSVEP-BCI task. What we could observe was the increasing EEG power of the target frequency from the user’s visual area. BCI user’s cognitive state changes, especially in mental focus state or lost-in-thought state, will affect the BCI performance in sustained usage of SSVEP. Therefore, how to differentiate BCI users’ physiological state through exploring their neural activities changes while performing SSVEP is a key technology for enhancing the BCI performance. In this study, we designed a new BCI experiment which combined working memory task into the flashing targets of SSVEP task using 12 Hz or 30 Hz frequencies. Through exploring the EEG activity changes corresponding to the working memory and SSVEP task performance, we can recognize if the user’s cognitive state is in mental focus or lost-in-thought. Experiment results show that the delta (1–4 Hz), theta (4–7 Hz), and beta (13–30 Hz) EEG activities increased more in mental focus than in lost-in-thought state at the frontal lobe. In addition, the powers of the delta (1–4 Hz), alpha (8–12 Hz), and beta (13–30 Hz) bands increased more in mental focus in comparison with the lost-in-thought state at the occipital lobe. In addition, the average classification performance across subjects for the KNN and the Bayesian network classifiers were observed as 77% to 80%. These results show how mental state changes affect the performance of BCI users. In this work, we developed a new scenario to recognize the user’s cognitive state during performing BCI tasks. These findings can be used as the novel neural markers in future BCI developments.

scholarly journals Tangent Space Features-Based Transfer Learning Classification Model for Two-Class Motor Imagery Brain–Computer Interface

2019 ◽  
Vol 29 (10) ◽  
pp. 1950025 ◽  
Author(s):  
Pramod Gaur ◽  
Karl McCreadie ◽  
Ram Bilas Pachori ◽  
Hui Wang ◽  
Girijesh Prasad
Keyword(s):  
Transfer Learning ◽  
Motor Imagery ◽  
Tangent Space ◽  
Brain Computer Interface ◽  
Training Session ◽  
Training Data ◽  
Classification Model ◽  
Computer Interface ◽  
Multivariate Empirical Mode Decomposition ◽  
Subject Specific

The performance of a brain–computer interface (BCI) will generally improve by increasing the volume of training data on which it is trained. However, a classifier’s generalization ability is often negatively affected when highly non-stationary data are collected across both sessions and subjects. The aim of this work is to reduce the long calibration time in BCI systems by proposing a transfer learning model which can be used for evaluating unseen single trials for a subject without the need for training session data. A method is proposed which combines a generalization of the previously proposed subject-specific “multivariate empirical-mode decomposition” preprocessing technique by taking a fixed band of 8–30[Formula: see text]Hz for all four motor imagery tasks and a novel classification model which exploits the structure of tangent space features drawn from the Riemannian geometry framework, that is shared among the training data of multiple sessions and subjects. Results demonstrate comparable performance improvement across multiple subjects without subject-specific calibration, when compared with other state-of-the-art techniques.

scholarly journals USING STUDENT MENTAL STATE AND LEARNING SENSORY MODALITIES TO IMPROVE ADAPTIVITY IN E-LEARNING

2014 ◽  
Vol 2 (1) ◽  
Author(s):  
Paola C. Rodriguez ◽  
Fabio Paternò ◽  
Jovani Jimenez
Keyword(s):  
Mental State ◽  
Brain Computer Interface ◽  
Learning System ◽  
Computer Interface ◽  
Learning Resources ◽  
Sensory Modalities ◽  
Innovative Solution ◽  
E Learning

  In this paper, we present an innovative solution to improve adaptivity in an e-learning system using Brain Computer Interface (BCI) measures (Attention/Meditation) in order to detect changes in students’ preferred perceptual modes for learning information (VARK model). Our solution is also able to report course units and learning resources that could be difficult for the students.

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