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.

On a Gamified Brain-Computer Interface for Cognitive Training of Spatial Working Memory

2020 ◽  
Author(s):  
Ziming Liu ◽  
Jonathan Bryan ◽  
Robert Borkoski ◽  
Fengpei Yuan ◽  
Yansong Li ◽  
...  
Keyword(s):  
Working Memory ◽  
Cognitive Training ◽  
Attention Deficit ◽  
Spatial Working Memory ◽  
Brain Computer Interface ◽  
Body Part ◽  
The United States ◽  
Attention Problems ◽  
Computer Interface ◽  
Memory And Attention

Abstract In the United States, there are a large number of people suffering from memory and attention deficit problems. For example, patients with attention-deficit hyperactivity disorder (ADHD) and dementia have difficulties in performing activities of daily living and have a low quality of life. Currently, there exist no effective treatment for these memory and attention issues in specific cognitive impairments. In this paper, we developed a gamified platform of brain-computer interface (BCI) for cognitive training, which can engage users in the training and provide users qualitative and quantitative feedback for their training of spatial working memory. The user is able to control the movement of a drone using motor imager, which is imagined movement of body part. Sensorimotor rhythms of the user are calculated using the user’s EEG to drive the movement of the drone. Twenty normal healthy subjects were recruited to test the user experience. Our system showed the capability of engaging users, good robustness, user acceptability and usability. Therefore, we think our platform might be an alternative to provide more accessible, engaging, and effective cognitive training for people with memory and attention problems. In future, we will test the usability and effectiveness of the system for cognitive training in patients with ADHD and dementia.

Examining Positivity Effect and Working Memory in Young-Old and Very Old Adults Using EEG-Derived Cognitive State Metrics

2019 ◽  
Vol 41 (10) ◽  
pp. 1014-1035
Author(s):  
Joelle C. Ruthig ◽  
Dmitri P. Poltavski ◽  
Thomas Petros
Keyword(s):  
Older Adults ◽  
Working Memory ◽  
Memory Task ◽  
Cognitive State ◽  
Emotional Stimuli ◽  
Very Old ◽  
Positivity Effect ◽  
Old Adults ◽  
Age Related ◽  
Young Old

The positivity effect among older adults is a tendency to process more positive and/or less negative emotional stimuli compared to younger adults, with unknown upper age boundaries. Cognitive and emotional working memory were assessed in young-old adults (60–75) and very old adults (VOAs; 80+) to determine whether emotional working memory declines similar to the age-related decline of cognitive working memory. The moderating role of valence on the link between age and emotional working memory was examined to identify change in positivity effect with advanced age. Electroencephalography (EEG) markers of cognitive workload and engagement were obtained to test the theory of cognitive resource allocation in older adults’ emotional stimuli processing. EEG recordings were collected during cognitive memory task and emotional working memory tasks that required rating emotional intensity of images pairs. Results indicate a positivity effect among VOAs that does not require additional cognitive effort and is not likely to diminish with age.

scholarly journals Neural correlates of executive function and working memory in the ‘at-risk mental state’

2009 ◽  
Vol 194 (1) ◽  
pp. 25-33 ◽  
Author(s):  
Matthew R. Broome ◽  
Pall Matthiasson ◽  
Paolo Fusar-Poli ◽  
James B. Woolley ◽  
Louise C. Johns ◽  
...  
Keyword(s):  
At Risk ◽  
Working Memory ◽  
Mental State ◽  
Verbal Fluency ◽  
Risk Group ◽  
Memory Task ◽  
Anterior Cingulate ◽  
Verbal Fluency Task ◽  
Fluency Task ◽  
At Risk Mental State

BackgroundPeople with prodromal symptoms have a very high risk of developing psychosis.AimsTo use functional magnetic resonance imaging to examine the neurocognitive basis of this vulnerability.MethodCross-sectional comparison of regional activation in individuals with an ‘at-risk mental state’ (at-risk group: n=17), patients with first-episode schizophreniform psychosis (psychosis group: n=10) and healthy volunteers (controls: n=15) during an overt verbal fluency task and an N-back working memory task.ResultsA similar pattern of between-group differences in activation was evident across both tasks. Activation in the at-risk group was intermediate relative to that in controls and the psychosis group in the inferior frontal and anterior cingulate cortex during the verbal fluency task and in the inferior frontal, dorsolateral prefrontal and parietal cortex during the N-back task.ConclusionsThe at-risk mental state is associated with abnormalities of regional brain function that are qualitatively similar to, but less severe than, those in patients who have recently presented with psychosis.

scholarly journals A Survey in Implementation and Applications of Electroencephalograph (EEG)-Based Brain-Computer Interface

2021 ◽  
Vol 39 (7) ◽  
pp. 1117-1132
Author(s):  
Samaa S. Abdulwahab ◽  
Hussain K. Khleaf ◽  
Manal H. Jassim
Keyword(s):  
Brain Computer Interface ◽  
Physical World ◽  
The Body ◽  
Computer Interface ◽  
Signal Acquisition ◽  
Rule Based ◽  
The Past ◽  
Brain Signals ◽  
Translation Systems ◽  
The Brain

A Brain-Computer Interface (BCI) is an external system that controls activities and processes in the physical world based on brain signals. In Passive BCI, artificial signals are automatically generated by a computer program without any input from nerves in the body. This is useful for individuals with mobility issues. Traditional BCI has been dependent only on recording brain signals with Electroencephalograph (EEG) and has used a rule-based translation algorithm to generate control commands. These systems have developed very accurate translation systems. This paper is about the different methods for adapting the signals from the brain. It has been mentioned that various kinds of surveys in the past to serve the purpose of the present research. This paper shows a simple and easy analysis of each technique and its respective benefits and drawbacks, including signal acquisition, signal pre-processing, feature classification and classification. Finally,  discussed is the application of EEG-based BCI.

Competing at the Cybathlon Championship for Athletes With Disabilities: Long-Term Motor Imagery Brain-Computer Interface Training of a Tetraplegic Cybathlete

2021 ◽  
Author(s):  
Attila Korik ◽  
Karl McCreadie ◽  
Niall McShane ◽  
Naomi Du Bois ◽  
Massoud Khodadadzadeh ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Cognitive State ◽  
Contributing Factors ◽  
Performance Pressure ◽  
Cord Injury ◽  
Athletes With Disabilities ◽  
Run Up ◽  
And Performance

Abstract Background: The brain-computer interface (BCI) race at the Cybathlon championship for athletes with disabilities challenges teams (BCI researchers, developers and pilots with spinal cord injury) to control an avatar on a virtual racetrack without movement. Here we describe the training regime and results of the Ulster University BCI Team pilot who is tetraplegic and has trained to use an electroencephalography (EEG)-based BCI intermittently over 10 years, to compete in three Cybathlon events. Methods: A multi-class, multiple binary classifier framework was used to decode three kinesthetically imagined movements (motor imagery) (left (L) and right (R) arm and feet (F)) as well as relax state (X). Three games paradigms were used for training i.e., NeuroSensi, Triad, and Cybathlon: BrainDriver. An evaluation of the pilot’s performance is presented for two Cybathlon competition training periods – spanning 20 sessions over 5 weeks prior to the 2019 competition, and 25 sessions over 5 weeks in the run up to the 2020 competition.Results: Having participated in BCI training in 2009 and competed in Cybathlon 2016, the experienced pilot achieved high two-class accuracy on all class pairs when training began in 2019 (decoding accuracy >90%, resulting in efficient NeuroSensi and Triad game control). The BrainDriver performance (i.e., Cybathlon race completion time) improved significantly during the training period, leading up to the competition day, ranging from 274s - 156s (255±24s to 191±14s mean±std), over 17 days (10 sessions) in 2019, and from 230s - 168s (214±14s to 181±4s), over 18 days (13 sessions) in 2020. However, on both competition occasions, towards the race date, the performance deteriorated significantly.Conclusions: The training regime and framework applied were highly effective in achieving competitive race completion times. The BCI framework did not cope with significant deviation in electroencephalography (EEG) observed in the sessions occurring shortly before and during the race day. Stress, arousal level and fatigue, associated with the competition challenge and performance pressure resulting in cognitive state changes, were likely contributing factors to the nonstationary effects that resulted in the BCI and pilot achieving suboptimal performance on race day. Trial registration: not registered

scholarly journals Classification of BCI Users Based on Cognition

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
N. Firat Ozkan ◽  
Emin Kahya
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Cognitive State ◽  
Blink Rate ◽  
Computer Interfaces ◽  
Skin Response ◽  
Nasa Tlx ◽  
Healthy Participants ◽  
And Task

Brain-Computer Interfaces (BCI) are systems originally developed to assist paralyzed patients allowing for commands to the computer with brain activities. This study aims to examine cognitive state with an objective, easy-to-use, and easy-to-interpret method utilizing Brain-Computer Interface systems. Seventy healthy participants completed six tasks using a Brain-Computer Interface system and participants’ pupil dilation, blink rate, and Galvanic Skin Response (GSR) data were collected simultaneously. Participants filled Nasa-TLX forms following each task and task performances of participants were also measured. Cognitive state clusters were created from the data collected using the K-means method. Taking these clusters and task performances into account, the general cognitive state of each participant was classified as low risk or high risk. Logistic Regression, Decision Tree, and Neural Networks were also used to classify the same data in order to measure the consistency of this classification with other techniques and the method provided a consistency between 87.1% and 100% with other techniques.

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