Dataset Information

A comprehensive dataset for home appliance control using ERP-based BCIs with the application of inter-subject transfer learning.

ABSTRACT: Brain-computer interfaces (BCIs) have a potential to revolutionize human-computer interaction by enabling direct links between the brain and computer systems. Recent studies are increasingly focusing on practical applications of BCIs-e.g., home appliance control just by thoughts. One of the non-invasive BCIs using electroencephalography (EEG) capitalizes on event-related potentials (ERPs) in response to target stimuli and have shown promise in controlling home appliance. In this paper, we present a comprehensive dataset of online ERP-based BCIs for controlling various home appliances in diverse stimulus presentation environments. We collected online BCI data from a total of 84 subjects among whom 60 subjects controlled three types of appliances (TV: 30, door lock: 15, and electric light: 15) with 4 functions per appliance, 14 subjects controlled a Bluetooth speaker with 6 functions via an LCD monitor, and 10 subjects controlled air conditioner with 4 functions via augmented reality (AR). Using the dataset, we aimed to address the issue of inter-subject variability in ERPs by employing the transfer learning in two different approaches. The first approach, "within-paradigm transfer learning," aimed to generalize the model within the same paradigm of stimulus presentation. The second approach, "cross-paradigm transfer learning," involved extending the model from a 4-class LCD environment to different paradigms. The results demonstrated that transfer learning can effectively enhance the generalizability of BCIs based on ERP across different subjects and environments.

SUBMITTER: Lee J

PROVIDER: S-EPMC10867822 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature

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A comprehensive dataset for home appliance control using ERP-based BCIs with the application of inter-subject transfer learning.

Lee Jongmin J Kim Minju M Heo Dojin D Kim Jongsu J Kim Min-Ki MK Lee Taejun T Park Jongwoo J Kim HyunYoung H Hwang Minho M Kim Laehyun L Kim Sung-Phil SP

Frontiers in human neuroscience 20240201

Brain-computer interfaces (BCIs) have a potential to revolutionize human-computer interaction by enabling direct links between the brain and computer systems. Recent studies are increasingly focusing on practical applications of BCIs-e.g., home appliance control just by thoughts. One of the non-invasive BCIs using electroencephalography (EEG) capitalizes on event-related potentials (ERPs) in response to target stimuli and have shown promise in controlling home appliance. In this paper, we presen ...[more]

PMID: 38361913

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Project description:The stop signal task has been used to quantify the human inhibitory control. The inter-subject and intra-subject variability was investigated under the inhibition of human response with a realistic environmental scenario. In present study, we used a battleground scenario where a sniper-scope picture was the background, a target picture was a go signal, and a nontarget picture was a stop signal. The task instructions were to respond on the target image and inhibit the response if a nontarget image appeared. This scenario produced a threatening situation and endorsed the evaluation of how subject's response inhibition manifests in a real situation. In this study, 32 channels of electroencephalography (EEG) signals were collected from 20 participants during successful stop (response inhibition) and failed stop (response) trials. These EEG signals were used to predict two possible outcomes: successful stop or failed stop. The inter-subject variability (between-subjects) and intra-subject variability (within-subjects) affect the performance of participants in the classification system. The EEG signals of successful stop versus failed stop trials were classified using quadratic discriminant analysis (QDA) and linear discriminant analysis (LDA) (i.e., parametric) and K-nearest neighbor classifier (KNNC) and Parzen density-based (PARZEN) (i.e., nonparametric) under inter- and intra-subject variability. The EEG activities were found to increase during response inhibition in the frontal cortex (F3 and F4), presupplementary motor area (C3 and C4), parietal lobe (P3 and P4), and occipital (O1 and O2) lobe. Therefore, power spectral density (PSD) of EEG signals (1-50Hz) in F3, F4, C3, C4, P3, P4, O1, and O2 electrodes were measured in successful stop and failed stop trials. The PSD of the EEG signals was used as the feature input for the classifiers. Our proposed method shows an intra-subject classification accuracy of 97.61% for subject 15 with QDA classifier in C3 (left motor cortex) and an overall inter-subject classification accuracy of 71.66% ± 9.81% with the KNNC classifier in F3 (left frontal lobe). These results display how inter-subject and intra-subject variability affects the performance of the classification system. These findings can be used effectively to improve the psychopathology of attention deficit hyperactivity disorder (ADHD), obsessive-compulsive disorder (OCD), schizophrenia, and suicidality.

Project description:BackgroundTo explore the effect of applying a comprehensive unit-based safety program (CUSP) in the intrahospital transfer of patients with critical diseases.MethodsA total of 426 critically ill patients in the first affiliated Hospital of Anhui Medical University from August 2018 to February 2019 were divided into two groups according to the time of admission. Overall, 202 patients in the control group were treated with the routine transfer method, and 224 patients in the observational group were treated with the transfer method based on the CUSP model. The safety culture assessment data of medical staff, the occurrence rate of adverse events and related causes, the time of transfer, and the satisfaction of patients' relatives to the transfer process were compared before and after implementation of the transfer model between the two groups.ResultsBefore and after the implementation of the CUSP mode transfer program, there were significant differences in the scores of all dimensions of the safety culture assessment of medical staff (P < 0.05), and the occurrence rate of adverse events and the causes in the observational group were significantly lower than those in the control group (disease-related, staff-related, equipment-related, environment-related) (P < 0.05). The transfer time for Computed Tomography (CT), Magnetic Resonance Imaging (MRI), operating room, and the interventional room was significantly shorter in the observational group than that in the control group (P < 0.05), while the satisfaction of relatives to the transfer process was significantly higher than those in the control group (P < 0.05).ConclusionThe implementation of CUSP model for the intrahospital transfer of critically ill patients can significantly shorten the in-hospital transfer time, improve the attitude of medical staff towards safety, reduce the occurrence rate of adverse events, and improve the satisfaction of patients' relatives to the transfer process.

Dataset Information

A comprehensive dataset for home appliance control using ERP-based BCIs with the application of inter-subject transfer learning.

Publications

A comprehensive dataset for home appliance control using ERP-based BCIs with the application of inter-subject transfer learning.

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