scholarly journals Eye Blink Classification for Assisting Disability to Communicate Using Bagging and Boosting

2021 ◽  
Vol 5 (4) ◽  
pp. 117
Author(s):  
Luthfi Ardi ◽  
Noor Akhmad Setiawan ◽  
Sunu Wibirama
Keyword(s):  
Random Forest ◽  
Brain Computer Interface ◽  
Peak Amplitude ◽  
Computer Interface ◽  
Primary Data ◽  
Communication Tool ◽  
Mental Impairment ◽  
Eye Blinks ◽  
Accuracy Result ◽  
The Difference

Disability is a physical or mental impairment. People with disability have more barriers to do certain activity than those without disability. Moreover, several conditions make them having difficulty to communicate with other people. Currently, researchers have helped people with disabilities by developing brain-computer interface (BCI) technology, which uses artifact on electroencephalograph (EEG) as a communication tool using blinks. Research on eye blinks has only focused on the threshold and peak amplitude, while the difference in how many blinks can be detected using peak amplitude has not been the focus yet. This study used primary data taken using a Muse headband on 15 subjects. This data was used as a dataset classified using bagging (random forest) and boosting (XGBoost) methods with python; 80% of the data was allocated for learning and 20% was for testing. The classified data was divided into ten times of testing, which were then averaged. The number of eye blinks’ classification results showed that the accuracy value using random forest was 77.55%, and the accuracy result with the XGBoost method was 90.39%. The result suggests that the experimental model is successful and can be used as a reference for making applications that help people to communicate by differentiating the number of eye blinks. This research focused on developing the number of eye blinks. However, in this study, only three blinking were used so that further research could increase these number.

scholarly journals A Bipolar-Channel Hybrid Brain-Computer Interface System for Home Automation Control Utilizing Steady-State Visually Evoked Potential and Eye-Blink Signals

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5474 ◽  
Author(s):  
Dalin Yang ◽  
Trung-Hau Nguyen ◽  
Wan-Young Chung
Keyword(s):  
Steady State ◽  
Evoked Potential ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Home Automation ◽  
Eye Blink ◽  
Eye Blinks ◽  
Visually Evoked Potential ◽  
Automation Control ◽  
Bci System

The goal of this study was to develop and validate a hybrid brain-computer interface (BCI) system for home automation control. Over the past decade, BCIs represent a promising possibility in the field of medical (e.g., neuronal rehabilitation), educational, mind reading, and remote communication. However, BCI is still difficult to use in daily life because of the challenges of the unfriendly head device, lower classification accuracy, high cost, and complex operation. In this study, we propose a hybrid BCI system for home automation control with two brain signals acquiring electrodes and simple tasks, which only requires the subject to focus on the stimulus and eye blink. The stimulus is utilized to select commands by generating steady-state visually evoked potential (SSVEP). The single eye blinks (i.e., confirm the selection) and double eye blinks (i.e., deny and re-selection) are employed to calibrate the SSVEP command. Besides that, the short-time Fourier transform and convolution neural network algorithms are utilized for feature extraction and classification, respectively. The results show that the proposed system could provide 38 control commands with a 2 s time window and a good accuracy (i.e., 96.92%) using one bipolar electroencephalogram (EEG) channel. This work presents a novel BCI approach for the home automation application based on SSVEP and eye blink signals, which could be useful for the disabled. In addition, the provided strategy of this study—a friendly channel configuration (i.e., one bipolar EEG channel), high accuracy, multiple commands, and short response time—might also offer a reference for the other BCI controlled applications.

scholarly journals Difference in Somatosensory Event-Related Potentials in the Blind Subjects Leads to Better Performance in Tactile P300 BCI

2021 ◽  
Author(s):  
Rafael Grigoryan ◽  
Dariya Goranskaya ◽  
Andrey Demchinsky ◽  
Ksenia Ryabova ◽  
Denis Kuleshov ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Target Selection ◽  
Event Related Potentials ◽  
Event Related Potential ◽  
Computer Interface ◽  
Specific Task ◽  
Related Potentials ◽  
The Difference ◽  
Braille Display ◽  
Cognitive Event

Abstract In this study, we created 8-command P300 tactile brain-computer interface, running on minimally modified consumer Braille display, and tested it on 10 blind subjects and 10 sighted controls with two stimuli types, differing in size. Larger stimuli provide better BCI performance both in blind and sighted participants than smaller stimuli. With large stimuli, median target selection accuracy in the blind group was 95%, which is 27% more than sighted controls (p < 0.05), suggesting that blind subjects are not only able to use tactile brain-computer interface but also can achieve superior results in comparison with sighted subjects. The difference in event-related potentials between groups is located in frontocentral sites around 300 ms post-stimulus and corresponds with early cognitive event-related potential components. Blind subjects have higher amplitude and shorter latency of ERPs. This effect was consistent across stimuli types. This is the first study to evaluate differences in event-related potentials between blind and sighted subjects in a BCI-specific task.

scholarly journals Brain-computer-interface technology with multisensory feedback for controlled ideomotor training in the rehabilitation of stroke patients

2019 ◽  
pp. 27-32
Author(s):  
Yu.V. Bushkova ◽  
G.E. Ivanova ◽  
L.V. Stakhovskaya ◽  
A.A. Frolov
Keyword(s):  
Motor Imagery ◽  
Upper Limb ◽  
Brain Computer Interface ◽  
Motor Recovery ◽  
Computer Interface ◽  
Robotic Hand ◽  
Stroke Patients ◽  
Factors Affecting ◽  
The Difference ◽  
Recovery Dynamics

Motor recovery of the upper limb is a priority in the neurorehabilitation of stroke patients. Advances in the brain-computer interface (BCI) technology have significantly improved the quality of rehabilitation. The aim of this study was to explore the factors affecting the recovery of the upper limb in stroke patients undergoing BCI-based rehabilitation with the robotic hand. The study recruited 24 patients (14 men and 10 women) aged 51 to 62 years with a solitary supratentorial stroke lesion. The lesion was left-hemispheric in 11 (45.6%) patients and right-hemispheric in 13 (54.4%) patients. Time elapsed from stroke was 4.0 months (3.0; 12.0). The median MoCa score was 25.0 (23.0; 27.0). The rehabilitation course consisted of 9.5 sessions (8.0; 10.0). We established a significant moderate correlation between motor imagery performance (the MIQ-RS score) and the efficacy of patient-BCI interaction. Patients with high MIQ-RS scores (47.5 (32.0; 54.0) achieved a better control of the BCI-driven hand exoskeleton (63.0 (54.0; 67.0), R = 0.67; p < 0.05). Recovery dynamics were more pronounced in patients with high MIQ-RS scores: the median score on the Fugl-Meyer Assessment scale was 14 (8.0; 16.0) points vs 10 (6.0; 13.0) points in patients with low MIQ-RS scores. However, the difference was not significant. Thus, we established a correlation between a patient’s ability for motor imagery (MIQ-RS) and the efficacy of patient-BCI interaction. A larger patient sample might be necessary to assess the effect of these factors on motor recovery dynamics.

scholarly journals Advanced LabVIEW Applications Aimed for the Acquisition, Processing and Classification of the Electroencephalographic Signals Used in a Brain-Computer Interface System

2021 ◽  
Author(s):  
Oana Andreea Rușanu
Keyword(s):  
Real Time ◽  
Brain Computer Interface ◽  
Automatic Generation ◽  
Computer Interface ◽  
Eeg Signal ◽  
Eye Blink ◽  
Eye Blinks ◽  
Interface System ◽  
Temporal Sequences

This paper proposes several LabVIEW applications to accomplish the data acquisition, processing, features extraction and real-time classification of the electroencephalographic (EEG) signal detected by the embedded sensor of the NeuroSky Mindwave Mobile headset. The LabVIEW applications are aimed at the implementation of a Brain-Computer Interface system, which is necessary to people with neuromotor disabilities. It is analyzed a novel approach regarding the preparation and automatic generation of the EEG dataset by identifying the most relevant multiple mixtures between selected EEG rhythms (both time and frequency domains of raw signal, delta, theta, alpha, beta, gamma) and extracted statistical features (mean, median, standard deviation, route mean square, Kurtosis coefficient and others). The acquired raw EEG signal is processed and segmented into temporal sequences corresponding to the detection of the multiple voluntary eye-blinks EEG patterns. The main LabVIEW application accomplished the optimal real-time artificial neural networks techniques for the classification of the EEG temporal sequences corresponding to the four states: 0 - No Eye-Blink Detected; 1 - One Eye-Blink Detected; 2 &ndash; Two Eye-Blinks Detected and 3 &ndash; Three Eye-Blinks Detected. Nevertheless, the application can be used to classify other EEG patterns corresponding to different cognitive tasks, since the whole functionality and working principle could estimate the labels associated with various classes.

scholarly journals Classification of motor imaginary in EEG using random

2017 ◽  
Vol 7 (3) ◽  
pp. 129-135
Author(s):  
Suat Karakaya ◽  
Gurkan Kucukyildiz ◽  
Hasan Ocak
Keyword(s):  
Random Forest ◽  
Classification Scheme ◽  
Brain Computer Interface ◽  
Binary Classification ◽  
Classification Tree ◽  
Computer Interface ◽  
Practical Application ◽  
New Classification ◽  
Eeg Data

Abstract   Although the motor-imagery-based brain computer interface (BCI) has become popular in recent years, its practical application is limited due to the classification accuracy of methods. In this study, a new classification scheme is proposed for the classification of multi-class motor imaginary in EEG using random forest (RF) classifier. In the proposed scheme, a four-stage binary classification tree is constructed. An RF model is trained for each stage of decision tree using features extracted from the EEG channels. The EEG band powers of each channel are the extracted features from the EEG signal. The proposed classification scheme is applied on the BCI competition IV dataset 2a recordings. The EEG data is acquired from nine subjects and the proposed scheme is performed for each subject independently. The kappa values of the proposed scheme are calculated to compare the results with the methods in the literature. It is demonstrated that the proposed classification scheme has higher kappa values than the methods in the literature. Keywords: Brain computer interface, motor imaginary, random forest.

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