scholarly journals An Inter- and Intra-Subject Transfer Calibration Scheme for Improving Feedback Performance of Sensorimotor Rhythm-Based BCI Rehabilitation

2021 ◽  
Vol 14 ◽  
Author(s):  
Lei Cao ◽  
Shugeng Chen ◽  
Jie Jia ◽  
Chunjiang Fan ◽  
Haoran Wang ◽  
...  
Keyword(s):  
Classification Performance ◽  
Computer Interface ◽  
Learning Approaches ◽  
Stroke Patients ◽  
Motor Disabilities ◽  
Time Consumption ◽  
Classifier Calibration ◽  
Machine Communication ◽  
The Brain ◽  
Bci System

The Brain Computer Interface (BCI) system is a typical neurophysiological application which helps paralyzed patients with human-machine communication. Stroke patients with motor disabilities are able to perform BCI tasks for clinical rehabilitation. This paper proposes an effective scheme of transfer calibration for BCI rehabilitation. The inter- and intra-subject transfer learning approaches can improve the low-precision classification performance for experimental feedback. The results imply that the systematical scheme is positive in increasing the confidence of voluntary training for stroke patients. In addition, it also reduces the time consumption of classifier calibration.

Designing Better, Cost-Effective Brain–Computer Interfaces

2015 ◽  
Vol 23 (4) ◽  
pp. 13-19 ◽  
Author(s):  
Chang S. Nam ◽  
Matthew Moore ◽  
Inchul Choi ◽  
Yueqing Li
Keyword(s):  
Steady State ◽  
Research And Development ◽  
Evoked Potential ◽  
Brain Computer Interface ◽  
Cost Effective ◽  
Computer Interface ◽  
Computer Interfaces ◽  
The Brain ◽  
Bci System ◽  
Users With Special Needs

Despite the increase in research interest in the brain–computer interface (BCI), there remains a general lack of understanding of, and even inattention to, human factors/ergonomics (HF/E) issues in BCI research and development. The goal of this article is to raise awareness of the importance of HF/E involvement in the emerging field of BCI technology by providing HF/E researchers with a brief guide on how to design and implement a cost-effective, steady-state visually evoked potential (SSVEP)–based BCI system. We also discuss how SSVEP BCI systems can be improved to accommodate users with special needs.

scholarly journals Application of Convolutional Neural Network Method in Brain Computer Interface

2021 ◽  
Vol 2078 (1) ◽  
pp. 012044
Author(s):  
Lingzhi Chen ◽  
Wei Deng ◽  
Chunjin Ji
Keyword(s):  
Pattern Recognition ◽  
Recognition Accuracy ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Eeg Signals ◽  
Network Method ◽  
Electroencephalogram Eeg ◽  
The Brain ◽  
Bci System

Abstract Pattern Recognition is the most important part of the brain computer interface (BCI) system. More and more profound learning methods were applied in BCI to increase the overall quality of pattern recognition accuracy, especially in the BCI based on Electroencephalogram (EEG) signal. Convolutional Neural Networks (CNN) holds great promises, which has been extensively employed for feature classification in BCI. This paper will review the application of the CNN method in BCI based on various EEG signals.

scholarly journals Electroencephalography Signal Grouping and Feature Classification Using Harmony Search for BCI

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Tae-Ju Lee ◽  
Seung-Min Park ◽  
Kwee-Bo Sim
Keyword(s):  
Signal Analysis ◽  
Harmony Search ◽  
Heuristic Method ◽  
Heuristic Approach ◽  
Computer Interface ◽  
Eeg Signal ◽  
Feature Classification ◽  
The Brain ◽  
Eeg Signal Analysis ◽  
Bci System

This paper presents a heuristic method for electroencephalography (EEG) grouping and feature classification using harmony search (HS) for improving the accuracy of the brain-computer interface (BCI) system. EEG, a noninvasive BCI method, uses many electrodes on the scalp, and a large number of electrodes make the resulting analysis difficult. In addition, traditional EEG analysis cannot handle multiple stimuli. On the other hand, the classification method using the EEG signal has a low accuracy. To solve these problems, we use a heuristic approach to reduce the complexities in multichannel problems and classification. In this study, we build a group of stimuli using the HS algorithm. Then, the features from common spatial patterns are classified by the HS classifier. To confirm the proposed method, we perform experiments using 64-channel EEG equipment. The subjects are subjected to three kinds of stimuli: audio, visual, and motion. Each stimulus is applied alone or in combination with the others. The acquired signals are processed by the proposed method. The classification results in an accuracy of approximately 63%. We conclude that the heuristic approach using the HS algorithm on the BCI is beneficial for EEG signal analysis.

scholarly journals Brain Computer Interface Controlled Voice Assistant

2020 ◽  
Vol 8 (6) ◽  
pp. 2370-2377
Keyword(s):  
Brain Activity ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Project Work ◽  
Daily Lives ◽  
Computer Interfaces ◽  
The Disabled ◽  
And Performance ◽  
The Brain ◽  
Bci System

A brain-controlled robot using brain computer interfaces (BCIs) was explored in this project. BCIs are systems that are able to circumvent traditional communication channels (i.e. muscles and thoughts), to ensure the human brain and physical devices communicate directly and are in charge by converting various patterns of brain activity to instructions in real time. An automation can be managed with these commands. The project work seeks to build and monitor a program that can help the disabled people accomplish certain activities independently of others in their daily lives. Develop open-source EEG and brain-computer interface analysis software. The quality and performance of BCI of different EEG signals are compared. Variable signals obtained through MATLAB Processing from the Brainwave sensor. Automation modules operate by means of the BCI system. The Brain Computer Interface aims to build a fast and reliable link between a person's brain and a personal computer. The controls also use the Brain-Computer Interface for home appliances. The system will integrate with any smartphones voice assistant.

scholarly journals Low-Cost Brain-Computer Interface Using the Emotiv Epoc Headset Based on Rotating Vanes

2020 ◽  
Vol 37 (5) ◽  
pp. 831-837
Author(s):  
Mesut Melek ◽  
Negin Manshouri ◽  
Temel Kayikcioglu
Keyword(s):  
Information Transfer ◽  
Brain Computer Interface ◽  
Low Cost ◽  
Computer Interface ◽  
Digital Signals ◽  
Eeg Signals ◽  
The Gaze ◽  
Information Transfer Rate ◽  
The Brain ◽  
Bci System

Detailed In the brain-computer interface system (BCI), electroencephalography (EEG) signals are converted into digital signals and analyzed, allowing direct communication between humans and the electronic devices around them. The convenience of the user and the speed of communication with the surrounding devices are the most important challenges of BCI systems. The Emotiv Epoc headset minimizes the discomfort of the user thanks to its wet electrodes and easy handling. In the continuation of our previous works, in this paper, we developed our BCI system based on the gaze at the rotating vanes using the inexpensive Emotiv Epoc headset. In addition to user comfort, our design has an acceptable mean accuracy rate (ACC) and mean information transfer rate (ITR) compared to similar systems.

scholarly journals PRINCIPLES OF MOTOR RECOVERY IN POST-STROKE PATIENTS USING HAND EXOSKELETON CONTROLLED BY THE BRAIN-COMPUTER INTERFACE BASED ON MOTOR IMAGERY

2017 ◽  
Vol 27 (1) ◽  
pp. 107-137 ◽  
Author(s):  
A. A. Frolov ◽  
Dušan Húsek ◽  
E. V. Biryukova ◽  
P. D. Bobrov ◽  
O. A. Mokienko ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Brain Computer Interface ◽  
Motor Recovery ◽  
Computer Interface ◽  
Stroke Patients ◽  
Post Stroke ◽  
Hand Exoskeleton ◽  
The Brain

scholarly journals Electroencephalogram with Machine Learning for Estimation of Mental Confusion Level

2019 ◽  
Vol 9 (2) ◽  
pp. 761-765
Keyword(s):  
Machine Learning ◽  
Concentration Level ◽  
Brain Computer Interface ◽  
Quantitative Measure ◽  
Threshold Value ◽  
Machine Learning Algorithms ◽  
Computer Interface ◽  
Eeg Data ◽  
The Brain ◽  
Bci System

Estimating the mental state of an individual is crucial to many applications. A quantitative measure of the confusion one faces while doing a task can be useful in determining which subtask is the most difficult. This paper thus aims to develop an algorithm to estimate the confusion score using EEG signals collected using a Neurosky Mindwave Headset. Also, a full contextual audio based confusion score is generated to improve the system's resilience. In this paper, the final algorithm is used to propose an EEG based system to enable the UI/UX testing which can help in confusion estimation and thus provide a qualitative means to measure the attention and concentration level of people which can be extended to various applications. The raw EEG data collected from the device was used to calculate the confusion score using various Machine Learning algorithms. This brain computer interface (BCI) system can be extended for calculating the confusion score of a person which can be used for various applications such as teaching, child health monitoring, suicide prevention, mental health analysis etc. The brain computer interface thus calculates the confusion score and based on the threshold value of the attention and concentration level it performs certain actions such as sending messages and alerts to emergency contacts. This is further extended to solve the problem of Usability testing in Human Computer Interaction.

scholarly journals Facial Expression Decoding based on fMRI Brain Signal

2019 ◽  
Vol 14 (4) ◽  
pp. 475-488
Author(s):  
Benchun Cao ◽  
Yanchun Liang ◽  
Shinichi Yoshida ◽  
Renchu Guan
Keyword(s):  
Support Vector Machine ◽  
Extreme Learning Machine ◽  
Facial Expressions ◽  
Classification Performance ◽  
Computer Interface ◽  
Support Vector ◽  
Brain Decoding ◽  
Learning Machine ◽  
The Brain ◽  
Better Than

The analysis of facial expressions is a hot topic in brain-computer interface research. To determine the facial expressions of the subjects under the corresponding stimulation, we analyze the fMRI images acquired by the Magnetic Resonance. There are six kinds of facial expressions: "anger", "disgust", "sadness", "happiness", "joy" and "surprise". We demonstrate that brain decoding is achievable through the parsing of two facial expressions ("anger" and "joy"). Support vector machine and extreme learning machine are selected to classify these expressions based on time series features. Experimental results show that the classification performance of the extreme learning machine algorithm is better than support vector machine. Among the eight participants in the trials, the classification accuracy of three subjects reached 70-80%, and the remaining five subjects also achieved accuracy of 50-60%. Therefore, we can conclude that the brain decoding can be used to help analyzing human facial expressions.

scholarly journals Home Automation using Brain Computer Interface for the Physically Challenged

2019 ◽  
Vol 9 (1) ◽  
pp. 5018-5021
Keyword(s):  
Brain Computer Interface ◽  
Brain Structures ◽  
Computer Interface ◽  
Brain Machine Interface ◽  
Home Automation ◽  
Eeg Signals ◽  
Machine Interface ◽  
Physically Challenged ◽  
The Brain ◽  
Bci System

A Brain-Computer Interface (BCI)is labeledas Mind-Machine Interface (MMI) or a Brain-Machine Interface (BMI). It affords a non-muscular channel of messagein between the computer and a human brain. Using the enhancements in interface equipment to electronics,and the necessity to helpindividuals suffering from disabilities, a new area in this study has begun by acceptingtasks of brain. The Electro-Encephalogram (EEG) is an electrical activity created by brain structures and verified from the scalp using electrodes. The EEG signal is used in actualspell to accomplishperipheral devices using a broad BCI system. The post-processed output signals are converted to suitable instructions to regulate output devices. The main seek is to aidparalyzed and physically immobilizedpersons to govern the home appliances making use of Electro-Encephalogram (EEG) signals, such that they grow to beautonomous. According to the brain responsiveness the devices can be designated then usingrelays, the switching of the home-basedmachinescan be completedconsequently.

scholarly journals CLINICAL EXPERIENCE OF POST-STROKE REHABILITATION WITH THE USE OF HAND EXOSKELETON CONTROLLED BY BRAIN-COMPUTER INTERFACE

2016 ◽  
Vol 1 (3) ◽  
pp. 56-61
Author(s):  
A A Frolov ◽  
O A Mokienko ◽  
E V Biryukova ◽  
P D Bobrov ◽  
R Kh Lukmanov ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Control Group ◽  
Stroke Patients ◽  
Motor Functions ◽  
Post Stroke ◽  
Significant Difference ◽  
Medical Computer ◽  
Hand Exoskeleton ◽  
The Brain

Aim - to evaluate the efficiency of the motor recovery rehabilitation procedure with the use of hand exoskeleton controlled by the brain-computer interface (BCI). Materials and methods. 60 post-stroke patients participated in the study. 46 patients had ischemic stroke and 14 had hemorrhagic stroke. 42 patients of the main experimental group were trained in kinesthetic motor imagery using hand exoskeleton controlled by BCI, 18 patients of the control group carried out the imitating procedure. Exoskeleton - BCI system consists of encephalograph NVX52 («Medical Computer Systems», Russia), personal computer and hand exoskeleton («Android Technique», Russia). Motor functions were estimated by neurological scales ARAT and Fugl-Meyer. Results were statistically analyzed by Mann-Whitney, Wilcoxon and x2 tests, Spearman's correlation and RM-ANOVA using Statsoft Statistica v. 6.0. Results. It is shown that post-stroke patients are able to control BCI with the same efficiency as healthy subjects, regardless of the duration, severity and localization of the disease. Ten days of BCI training significantly improved patients’ motor functions according to neurological scales ARAT and Fugl-Meyer. Improvement was mainly provided by the small movements of the hand. According to several sections of neurological scales, improvement in the main group is significantly higher than in the control group. However, according to general scores, statistically significant difference between two groups was not observed. Conclusion. It is shown that the rehabilitation procedure using hand exsoskeleton controlled by BCI significantly improves motor functions of the paretic arm regardless of the duration, severity and localization of the disease. Increase of the training duration enhances the rehabilitation efficiency.

Sign in / Sign up

Export Citation Format

Share Document