scholarly journals Exploring Fatigue Effects on Performance Variation of Intensive Brain–Computer Interface Practice

2021 ◽  
Vol 15 ◽  
Author(s):  
Songwei Li ◽  
Junyi Duan ◽  
Yu Sun ◽  
Xinjun Sheng ◽  
Xiangyang Zhu ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Mental States ◽  
Computer Interface ◽  
Alpha Band ◽  
Visual Fatigue ◽  
Eyes Closed ◽  
Band Power ◽  
Eyes Open ◽  
Rest Condition ◽  
Online Feedback

Motor imagery (MI) is an endogenous mental process and is commonly used as an electroencephalogram (EEG)-based brain–computer interface (BCI) strategy. Previous studies of P300 and MI-based (without online feedback) BCI have shown that mental states like fatigue can negatively affect participants’ EEG signatures. However, exogenous stimuli cause visual fatigue, which might have a different mechanism than endogenous tasks do. Furthermore, subjects could adjust themselves if online feedback is provided. In this sense, it is still unclear how fatigue affects online MI-based BCI performance. With this question, 12 healthy subjects are recruited to investigate this issue, and an MI-based online BCI experiment is performed for four sessions on different days. The first session is for training, and the other three sessions differ in rest condition and duration—no rest, 16-min eyes-open rest, and 16-min eyes-closed rest—arranged in a pseudo-random order. Multidimensional fatigue inventory (MFI) and short stress state questionnaire (SSSQ) reveal that general fatigue, mental fatigue, and distress have increased, while engagement has decreased significantly within certain sessions. However, the BCI performances, including percent valid correct (PVC) and information transfer rate (ITR), show no significant change across 400 trials. The results suggest that although the repetitive MI task has affected subjects’ mental states, their BCI performances and feature separability within a session are not affected by the task significantly. Further electrophysiological analysis reveals that the alpha-band power in the sensorimotor area has an increasing tendency, while event-related desynchronization (ERD) modulation level has a decreasing trend. During the rest time, no physiological difference has been found in the eyes-open rest condition; on the contrary, the alpha-band power increase and subsequent decrease appear in the eyes-closed rest condition. In summary, this experiment shows evidence that mental states can change dramatically in the intensive MI-BCI practice, but BCI performances could be maintained.

scholarly journals Home used, patient self-managed, brain-computer interface for the management of central neuropathic pain post spinal cord injury: usability study

2019 ◽  
Vol 16 (1) ◽  
Author(s):  
M. K. H. Al-Taleb ◽  
M. Purcell ◽  
M. Fraser ◽  
N. Petric-Gray ◽  
A. Vuckovic
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Neuropathic Pain ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Alpha Band ◽  
Central Neuropathic Pain ◽  
Band Power ◽  
Custom Made ◽  
Cord Injury

Abstract Background Central Neuropathic Pain (CNP) is a frequent chronic condition in people with spinal cord injury (SCI). Previously, we showed that using laboratory brain-computer interface (BCI) technology for neurofeedback (NFB) training, it was possible to reduce CNP in people with SCI. In this study, we show results of patient self-managed treatment in their homes with a BCI-NFB using a consumer EEG device. Methods Users: People with chronic SCI (17 M, 3 F, 50.6 ± 14.1 years old), and CNP ≥4 on a Visual Numerical Scale. Location: Laboratory training (up to 4 sessions) followed by home self-managed NFB. User Activity: Upregulating the EEG alpha band power by 10% above a threshold and at the same time downregulating the theta and upper beta (20-30 Hz) band power by 10% at electrode location C4. Technology: A consumer grade multichannel EEG headset (Epoch, Emotiv, USA), a tablet computer and custom made NFB software. Evaluation: EEG analysis, before and after NFB assessment, interviews and questionnaires. Results Effectiveness: Out of 20 initially assessed participants, 15 took part in the study. Participants used the system for 6.9 ± 5.5 (median 4) weeks. Twelve participants regulated their brainwaves in a frequency specific manner and were most successful upregulating the alpha band power. However they typically upregulated power around their individual alpha peak (7.6 ± 0.8 Hz) that was lower than in people without CNP. The reduction in pain experienced was statistically significant in 12 and clinically significant (greater than 30%) in 8 participants. Efficiency: The donning was between 5 and 15 min, and approximately 10–20% of EEG data recorded in the home environment was noise. Participants were mildly stressed when self-administering NFB at home (2.4 on a scale 1–10). User satisfaction: Nine participants who completed the final assessment reported a high level of satisfaction (QUESQ, 4.5 ± 0.8), naming effectiveness, ease of use and comfort as main priorities. The main factors influencing frequency of NFB training were: health related issues, free time and pain intensity. Conclusion Portable NFB is a feasible solution for home-based self-managed treatment of CNP. Compared to pharmacological treatments, NFB has less side effects and provides users with active control over pain. Trial registration GN15NE124, Registered 9th June 2016.

scholarly journals On the Feasibility of Using an Ear-EEG to Develop an Endogenous Brain-Computer Interface

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2856 ◽  
Author(s):  
Soo-In Choi ◽  
Chang-Hee Han ◽  
Ga-Young Choi ◽  
Jaeyoung Shin ◽  
Kwang Soup Song ◽  
...  
Keyword(s):  
Mental Arithmetic ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Brain Area ◽  
Computer Interface ◽  
Activity Increase ◽  
Eyes Closed ◽  
Eyes Open ◽  
External Stimuli ◽  
Bci System

Brain-computer interface (BCI) studies based on electroencephalography (EEG) measured around the ears (ear-EEGs) have mostly used exogenous paradigms involving brain activity evoked by external stimuli. The objective of this study is to investigate the feasibility of ear-EEGs for development of an endogenous BCI system that uses self-modulated brain activity. We performed preliminary and main experiments where EEGs were measured on the scalp and behind the ears to check the reliability of ear-EEGs as compared to scalp-EEGs. In the preliminary and main experiments, subjects performed eyes-open and eyes-closed tasks, and they performed mental arithmetic (MA) and light cognitive (LC) tasks, respectively. For data analysis, the brain area was divided into four regions of interest (ROIs) (i.e., frontal, central, occipital, and ear area). The preliminary experiment showed that the degree of alpha activity increase of the ear area with eyes closed is comparable to those of other ROIs (occipital > ear > central > frontal). In the main experiment, similar event-related (de)synchronization (ERD/ERS) patterns were observed between the four ROIs during MA and LC, and all ROIs showed the mean classification accuracies above 70% required for effective binary communication (MA vs. LC) (occipital = ear = central = frontal). From the results, we demonstrated that ear-EEG can be used to develop an endogenous BCI system based on cognitive tasks without external stimuli, which allows the usability of ear-EEGs to be extended.

scholarly journals Prediction of Individual User’s Dynamic Ranges of EEG Features from Resting-State EEG Data for Evaluating Their Suitability for Passive Brain–Computer Interface Applications

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 988
Author(s):  
Ho-Seung Cha ◽  
Chang-Hee Han ◽  
Chang-Hwan Im
Keyword(s):  
Resting State ◽  
Brain Computer Interface ◽  
Low Cost ◽  
Mental States ◽  
Computer Interface ◽  
Practical Applications ◽  
Eeg Data ◽  
Brain States ◽  
Electroencephalogram Eeg ◽  
Eeg Features

With the recent development of low-cost wearable electroencephalogram (EEG) recording systems, passive brain–computer interface (pBCI) applications are being actively studied for a variety of application areas, such as education, entertainment, and healthcare. Various EEG features have been employed for the implementation of pBCI applications; however, it is frequently reported that some individuals have difficulty fully enjoying the pBCI applications because the dynamic ranges of their EEG features (i.e., its amplitude variability over time) were too small to be used in the practical applications. Conducting preliminary experiments to search for the individualized EEG features associated with different mental states can partly circumvent this issue; however, these time-consuming experiments were not necessary for the majority of users whose dynamic ranges of EEG features are large enough to be used for pBCI applications. In this study, we tried to predict an individual user’s dynamic ranges of the EEG features that are most widely employed for pBCI applications from resting-state EEG (RS-EEG), with the ultimate goal of identifying individuals who might need additional calibration to become suitable for the pBCI applications. We employed a machine learning-based regression model to predict the dynamic ranges of three widely used EEG features known to be associated with the brain states of valence, relaxation, and concentration. Our results showed that the dynamic ranges of EEG features could be predicted with normalized root mean squared errors of 0.2323, 0.1820, and 0.1562, respectively, demonstrating the possibility of predicting the dynamic ranges of the EEG features for pBCI applications using short resting EEG data.

scholarly journals Radiofrequency signal affects alpha band in resting electroencephalogram

2015 ◽  
Vol 113 (7) ◽  
pp. 2753-2759 ◽  
Author(s):  
Rania Ghosn ◽  
Lydia Yahia-Cherif ◽  
Laurent Hugueville ◽  
Antoine Ducorps ◽  
Jean-Didier Lemaréchal ◽  
...  
Keyword(s):  
Salivary Cortisol ◽  
Spectral Power ◽  
Electrode Impedance ◽  
Alpha Band ◽  
Double Blind ◽  
Eyes Closed ◽  
Before And After ◽  
Sham Exposure ◽  
Eyes Open ◽  
Young Subjects

The aim of the present work was to investigate the effects of the radiofrequency (RF) electromagnetic fields (EMFs) on human resting EEG with a control of some parameters that are known to affect alpha band, such as electrode impedance, salivary cortisol, and caffeine. Eyes-open and eyes-closed resting EEG data were recorded in 26 healthy young subjects under two conditions: sham exposure and real exposure in double-blind, counterbalanced, crossover design. Spectral power of EEG rhythms was calculated for the alpha band (8–12 Hz). Saliva samples were collected before and after the study. Salivary cortisol and caffeine were assessed by ELISA and HPLC, respectively. The electrode impedance was recorded at the beginning of each run. Compared with the sham session, the exposure session showed a statistically significant ( P < 0.0001) decrease of the alpha band spectral power during closed-eyes condition. This effect persisted in the postexposure session ( P < 0.0001). No significant changes were detected in electrode impedance, salivary cortisol, and caffeine in the sham session compared with the exposure one. These results suggest that GSM-EMFs of a mobile phone affect the alpha band within spectral power of resting human EEG.

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