scholarly journals Robot Motion Control Using the Emotiv EPOC EEG System

2018 ◽  
Vol 7 (2) ◽  
pp. 279-285
Author(s):  
Sandy Akbar Dewangga ◽  
Handayani Tjandrasa ◽  
Darlis Herumurti
Keyword(s):  
Robot Control ◽  
Computer Interface ◽  
Support Vector ◽  
Robot Motion ◽  
Eeg Signals ◽  
Linear Discriminant ◽  
Computer Interfaces ◽  
Average Accuracy ◽  
The Subject ◽  
Electroencephalogram Eeg

Brain-computer interfaces have been explored for years with the intent of using human thoughts to control mechanical system. By capturing the transmission of signals directly from the human brain or electroencephalogram (EEG), human thoughts can be made as motion commands to the robot. This paper presents a prototype for an electroencephalogram (EEG) based brain-actuated robot control system using mental commands. In this study, Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) method were combined to establish the best model. Dataset containing features of EEG signals were obtained from the subject non-invasively using Emotiv EPOC headset. The best model was then used by Brain-Computer Interface (BCI) to classify the EEG signals into robot motion commands to control the robot directly. The result of the classification gave the average accuracy of 69.06%.

EEG Classification for BCI Based on CSP and SVM-GA

2013 ◽  
Vol 459 ◽  
pp. 228-231 ◽  
Author(s):  
Hao Yang ◽  
Song Wu
Keyword(s):  
Classification Performance ◽  
Computer Interface ◽  
Support Vector ◽  
Eeg Signals ◽  
Common Spatial Pattern ◽  
Data Set ◽  
Linear Discriminant ◽  
Brain Signals ◽  
Electroencephalogram Eeg ◽  
The Brain

Electroencephalogram (EEG) is generally used in Brain-Computer Interface (BCI) applications to measure the brain signals. However, the multichannel EEG signals characterized by unrelated and redundant features will deteriorate the classification accuracy. This paper presents a method based on common spatial pattern (CSP) for feature extraction and support vector machine with genetic algorithm (SVM-GA) as a classifier, the GA is used to optimize the kernel parameters setting. The proposed algorithm is performed on data set Iva of BCI Competition III. Results show that the proposed method outperforms the conventional linear discriminant analysis (LDA) in average classification performance.

scholarly journals A Comprehensive Machine-Learning-Based Software Pipeline to Classify EEG Signals: A Case Study on PNES vs. Control Subjects

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1235
Author(s):  
Giuseppe Varone ◽  
Sara Gasparini ◽  
Edoardo Ferlazzo ◽  
Michele Ascoli ◽  
Giovanbattista Gaspare Tripodi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical History ◽  
Processing Technique ◽  
Support Vector ◽  
Eeg Signals ◽  
Signal Processing Technique ◽  
Software Pipeline ◽  
Linear Discriminant ◽  
Eyes Closed ◽  
Average Accuracy

The diagnosis of psychogenic nonepileptic seizures (PNES) by means of electroencephalography (EEG) is not a trivial task during clinical practice for neurologists. No clear PNES electrophysiological biomarker has yet been found, and the only tool available for diagnosis is video EEG monitoring with recording of a typical episode and clinical history of the subject. In this paper, a data-driven machine learning (ML) pipeline for classifying EEG segments (i.e., epochs) of PNES and healthy controls (CNT) is introduced. This software pipeline consists of a semiautomatic signal processing technique and a supervised ML classifier to aid clinical discriminative diagnosis of PNES by means of an EEG time series. In our ML pipeline, statistical features like the mean, standard deviation, kurtosis, and skewness are extracted in a power spectral density (PSD) map split up in five conventional EEG rhythms (delta, theta, alpha, beta, and the whole band, i.e., 1–32 Hz). Then, the feature vector is fed into three different supervised ML algorithms, namely, the support vector machine (SVM), linear discriminant analysis (LDA), and Bayesian network (BN), to perform EEG segment classification tasks for CNT vs. PNES. The performance of the pipeline algorithm was evaluated on a dataset of 20 EEG signals (10 PNES and 10 CNT) that was recorded in eyes-closed resting condition at the Regional Epilepsy Centre, Great Metropolitan Hospital of Reggio Calabria, University of Catanzaro, Italy. The experimental results showed that PNES vs. CNT discrimination tasks performed via the ML algorithm and validated with random split (RS) achieved an average accuracy of 0.97 ± 0.013 (RS-SVM), 0.99 ± 0.02 (RS-LDA), and 0.82 ± 0.109 (RS-BN). Meanwhile, with leave-one-out (LOO) validation, an average accuracy of 0.98 ± 0.0233 (LOO-SVM), 0.98 ± 0.124 (LOO-LDA), and 0.81 ± 0.109 (LOO-BN) was achieved. Our findings showed that BN was outperformed by SVM and LDA. The promising results of the proposed software pipeline suggest that it may be a valuable tool to support existing clinical diagnosis.

A Review of Current Approaches of Brain Computer Interfaces

2012 ◽  
Vol 2 (2) ◽  
pp. 1-19
Author(s):  
Lochi Yu ◽  
Cristian Ureña
Keyword(s):  
Severe Disabilities ◽  
Extraction Methods ◽  
Support Vector ◽  
Eeg Signals ◽  
Linear Discriminant ◽  
Computer Interfaces ◽  
Vector Machines ◽  
Different Types ◽  
Computational Resources ◽  
The Brain

Since the first recordings of brain electrical activity more than 100 years ago remarkable contributions have been done to understand the brain functionality and its interaction with environment. Regardless of the nature of the brain-computer interface BCI, a world of opportunities and possibilities has been opened not only for people with severe disabilities but also for those who are pursuing innovative human interfaces. Deeper understanding of the EEG signals along with refined technologies for its recording is helping to improve the performance of EEG based BCIs. Better processing and features extraction methods, like Independent Component Analysis (ICA) and Wavelet Transform (WT) respectively, are giving promising results that need to be explored. Different types of classifiers and combination of them have been used on EEG BCIs. Linear, neural and nonlinear Bayesian have been the most used classifiers providing accuracies ranges between 60% and 90%. Some demand more computational resources like Support Vector Machines (SVM) classifiers but give good generality. Linear Discriminant Analysis (LDA) classifiers provide poor generality but low computational resources, making them optimal for some real time BCIs. Better classifiers must be developed to tackle the large patterns variability across different subjects by using every available resource, method or technology.

scholarly journals Kombinasi Sinyal EEG dan Giroskop untuk Kendali Mobil Virtual dengan Menggunakan Modifikasi ICA dan SVM

2016 ◽  
Vol 7 (3) ◽  
Author(s):  
Ahmad Reza Musthafa ◽  
Handayani Tjandrasa
Keyword(s):  
Support Vector Machine ◽  
Brain Computer Interface ◽  
Signal Classification ◽  
Computer Interface ◽  
Support Vector ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Artifact Removal ◽  
Eeg Signal Classification ◽  
Electroencephalogram Eeg

Abstract. Electroencephalogram (EEG) signals has been widely researched and developed in many fields of science. EEG signals could be classified into useful information for the application of Brain Computer Interface topic (BCI). In this research, we focus in a topic about driving a car using EEG signal. There are many approaches in EEG signal classification, but some approaches do not robust EEG signals that have many artifacts and have been recorded in real time. This research aims to classify EEG signals to obtain more optimal results, especially EEG signals with many artifacts and can be recorded in realtime. This research uses Emotiv EPOC device to record EEG signals in realtime. In this research, we propose the combination of Automatic Artifact Removal (AAR) and Support Vector Machine (SVM) which has 71% of accuracy that can be applied to drive a virtual car.Keyword: EEG signal classification, automatic artifact removal, brain computer interface Abstrak. Penelitian berbasis sinyal Electroencephalogram (EEG) telah banyak diteliti dan dikembangkan pada berbagai bidang ilmu pengetahuan. Sinyal EEG dapat diklasifikasikan ke dalam bentuk informasi untuk pengaplikasian topik Brain Computer Interface (BCI). Pada penelitian ini difokuskan pada topik pengendalian mobil menggunakan perintah sinyal EEG. Terdapat beberapa pendekatan dalam klasifikasi sinyal EEG, tetapi beberapa pendekatan tersebut tidak robust terhadap sinyal EEG yang memiliki banyak artefak dan direkam secara realtime. Penelitian ini bertujuan untuk mengklasifikasikan sinyal EEG dengan hasil lebih optimal, khususnya pada sinyal EEG yang memiliki banyak artefak dan direkam secara realtime. Penelitian ini menggunakan perangkat Emotiv EPOC untuk merekam sinyal EEG secara realtime. Pada penelitian ini diusulkan kombinasi Automatic Artifact Removal (AAR) dan Support Vector Machine (SVM) yang menghasilkan hasil akurasi sebesar 71% untuk klasifikasi sinyal EEG pada kasus pengendalian mobil virtual.Kata Kunci: EEG signal classification, automatic artifact removal, brain computer interface

scholarly journals Motor-Imagery EEG Signals Classificationusing SVM, MLP and LDA Classifiers

2021 ◽  
Vol 12 (2) ◽  
pp. 3339-3344
Author(s):  
Yogendra Narayan
Keyword(s):  
Human Subjects ◽  
Support Vector ◽  
Svm Classifier ◽  
Eeg Signals ◽  
Healthy Human ◽  
Linear Discriminant ◽  
Eeg Data ◽  
Mlp Classifier ◽  
Brain Computer Interfacing ◽  
Electroencephalogram Eeg

Electroencephalogram (EEG)signals based brain-computer interfacing (BCI) is the current technology trends in the field of rehabilitation robotic. This study compared the performance of support vector machine (SVM), linear discriminant analysis (LDA) and multi-layer perceptron (MLP) classifier with the combination of eight different features as a feature vector. EEG data were acquired from 20 healthy human subjects with predefined protocols. After the EEG signals acquisition, it was pre-processed followed by feature extraction and classification by using SVM MLP and LDA classifiers. The results exhibited that the SVM method was the best approach with 98.8% classification accuracy followed by MLP classifier. Finally, the SVM classifier and Arduino Mega controller was employed for offline controlling of the gripper of the robotic arm prototype. The finding of this study may be useful for online controlling as well as multi-degree of freedom with multi-class EEG dataset.

scholarly journals A Prototype SSVEP Based Real Time BCI Gaming System

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Ignas Martišius ◽  
Robertas Damaševičius
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machine Classifier ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Support Vector ◽  
Disabled People ◽  
Linear Discriminant ◽  
Average Accuracy ◽  
Wave Atom Transform ◽  
Linear Discriminant Analysis Classifier

Although brain-computer interface technology is mainly designed with disabled people in mind, it can also be beneficial to healthy subjects, for example, in gaming or virtual reality systems. In this paper we discuss the typical architecture, paradigms, requirements, and limitations of electroencephalogram-based gaming systems. We have developed a prototype three-class brain-computer interface system, based on the steady state visually evoked potentials paradigm and the Emotiv EPOC headset. An online target shooting game, implemented in the OpenViBE environment, has been used for user feedback. The system utilizes wave atom transform for feature extraction, achieving an average accuracy of 78.2% using linear discriminant analysis classifier, 79.3% using support vector machine classifier with a linear kernel, and 80.5% using a support vector machine classifier with a radial basis function kernel.

scholarly journals The MindGomoku: An Online P300 BCI Game Based on Bayesian Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1613
Author(s):  
Man Li ◽  
Feng Li ◽  
Jiahui Pan ◽  
Dengyong Zhang ◽  
Suna Zhao ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
System Control ◽  
Average Accuracy ◽  
Training Samples ◽  
The Disabled ◽  
Limited Training Samples ◽  
The Stability ◽  
Electroencephalogram Eeg ◽  
Bci System

In addition to helping develop products that aid the disabled, brain–computer interface (BCI) technology can also become a modality of entertainment for all people. However, most BCI games cannot be widely promoted due to the poor control performance or because they easily cause fatigue. In this paper, we propose a P300 brain–computer-interface game (MindGomoku) to explore a feasible and natural way to play games by using electroencephalogram (EEG) signals in a practical environment. The novelty of this research is reflected in integrating the characteristics of game rules and the BCI system when designing BCI games and paradigms. Moreover, a simplified Bayesian convolutional neural network (SBCNN) algorithm is introduced to achieve high accuracy on limited training samples. To prove the reliability of the proposed algorithm and system control, 10 subjects were selected to participate in two online control experiments. The experimental results showed that all subjects successfully completed the game control with an average accuracy of 90.7% and played the MindGomoku an average of more than 11 min. These findings fully demonstrate the stability and effectiveness of the proposed system. This BCI system not only provides a form of entertainment for users, particularly the disabled, but also provides more possibilities for games.

scholarly journals Self-Paced (Asynchronous) BCI Control of a Wheelchair in Virtual Environments: A Case Study with a Tetraplegic

2007 ◽  
Vol 2007 ◽  
pp. 1-8 ◽  
Author(s):  
Robert Leeb ◽  
Doron Friedman ◽  
Gernot R. Müller-Putz ◽  
Reinhold Scherer ◽  
Mel Slater ◽  
...  
Keyword(s):  
Virtual Environments ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Beta Oscillations ◽  
Brain Waves ◽  
The Subject ◽  
First Time ◽  
Electroencephalogram Eeg ◽  
Spinal Cord Injured

The aim of the present study was to demonstrate for the first time that brain waves can be used by a tetraplegic to control movements of his wheelchair in virtual reality (VR). In this case study, the spinal cord injured (SCI) subject was able to generate bursts of beta oscillations in the electroencephalogram (EEG) by imagination of movements of his paralyzed feet. These beta oscillations were used for a self-paced (asynchronous) brain-computer interface (BCI) control based on a single bipolar EEG recording. The subject was placed inside a virtual street populated with avatars. The task was to “go” from avatar to avatar towards the end of the street, but to stop at each avatar and talk to them. In average, the participant was able to successfully perform this asynchronous experiment with a performance of 90%, single runs up to 100%.

Online EEG Classification of Covert Speech for Brain–Computer Interfacing

2017 ◽  
Vol 27 (08) ◽  
pp. 1750033 ◽  
Author(s):  
Alborz Rezazadeh Sereshkeh ◽  
Robert Trott ◽  
Aurélien Bricout ◽  
Tom Chau
Keyword(s):  
Chance Level ◽  
Support Vector ◽  
Eeg Classification ◽  
Time Frequency ◽  
Computer Interfaces ◽  
Online Classification ◽  
Average Accuracy ◽  
Gamma Power ◽  
Brain Computer Interfacing

Brain–computer interfaces (BCIs) for communication can be nonintuitive, often requiring the performance of hand motor imagery or some other conversation-irrelevant task. In this paper, electroencephalography (EEG) was used to develop two intuitive online BCIs based solely on covert speech. The goal of the first BCI was to differentiate between 10[Formula: see text]s of mental repetitions of the word “no” and an equivalent duration of unconstrained rest. The second BCI was designed to discern between 10[Formula: see text]s each of covert repetition of the words “yes” and “no”. Twelve participants used these two BCIs to answer yes or no questions. Each participant completed four sessions, comprising two offline training sessions and two online sessions, one for testing each of the BCIs. With a support vector machine and a combination of spectral and time-frequency features, an average accuracy of [Formula: see text] was reached across participants in the online classification of no versus rest, with 10 out of 12 participants surpassing the chance level (60.0% for [Formula: see text]). The online classification of yes versus no yielded an average accuracy of [Formula: see text], with eight participants exceeding the chance level. Task-specific changes in EEG beta and gamma power in language-related brain areas tended to provide discriminatory information. To our knowledge, this is the first report of online EEG classification of covert speech. Our findings support further study of covert speech as a BCI activation task, potentially leading to the development of more intuitive BCIs for communication.

scholarly journals Context-Based Filtering for Assisted Brain-Actuated Wheelchair Driving

2007 ◽  
Vol 2007 ◽  
pp. 1-12 ◽  
Author(s):  
Gerolf Vanacker ◽  
José del R. Millán ◽  
Eileen Lew ◽  
Pierre W. Ferrez ◽  
Ferran Galán Moles ◽  
...  
Keyword(s):  
Control System ◽  
Shared Control ◽  
Eeg Signals ◽  
Intelligent Processing ◽  
Intelligent Wheelchair ◽  
Brain Signals ◽  
Brain Interface ◽  
The Subject ◽  
Electroencephalogram Eeg ◽  
The Brain

Controlling a robotic device by using human brain signals is an interesting and challenging task. The device may be complicated to control and the nonstationary nature of the brain signals provides for a rather unstable input. With the use of intelligent processing algorithms adapted to the task at hand, however, the performance can be increased. This paper introduces a shared control system that helps the subject in driving an intelligent wheelchair with a noninvasive brain interface. The subject's steering intentions are estimated from electroencephalogram (EEG) signals and passed through to the shared control system before being sent to the wheelchair motors. Experimental results show a possibility for significant improvement in the overall driving performance when using the shared control system compared to driving without it. These results have been obtained with 2 healthy subjects during their first day of training with the brain-actuated wheelchair.

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