Fast brain control systems for electric wheelchair using support vector machine

Parmonangan, Ivan Halim; Santoso, Jennifer; Budiharto, Widodo; Gunawan, Alexander Agung Santoso

doi:10.1117/12.2243126

Cited by 7 publications

(4 citation statements)

References 8 publications

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“…In this research, used of alpha (8-12 Hz) and beta waves by using the EMOTIV sensor with 14 channels, the classification success rate reaches 83-86% in distinguishing 5 types of imagery motors. However, the implementation is a bit complicated and inefficient in terms of channel usage which can be reduced further [7]. Other previous research on brain activity patterns were also conducted by Munawar et al In this research used Muse brain sensing which has four channels with the FFT method for feature extraction and the SVM method for classification which successfully classified five states to drive a wheeled robot with an accuracy rate of 91.78% [13].…”

Section: Introductionmentioning

confidence: 97%

“…In this era, Brain Computer Interface (BCI) has become a very interesting topic among researchers in the fields of * Corresponding author: munawar@elektro.undip.ac.id medicine, rehabilitation, health care, robotics, and entertainment [7]. BCI is a direct interface system from the brain to a computer or machine, which can receive commands directly from the brain.…”

Section: Introductionmentioning

confidence: 99%

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Implementation of Brain Computer Interface (BCI) as a Smart Wheelchair Motion Commands

Yuliana

Riyadi

2022

JBIOMES

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A wheelchair is a tool used to assist people with physical limitations in their legs. The most widely used are standard wheelchairs with a manual operating system by being pushed by hand. However, people with disabilities who have paralysis or suffer from neuromuscular and neurological conditions cannot use this wheelchair. Because of this, in this study focuses on implementing the Brain Computer Interface system to generate five commands to move a wheelchair. There are five important stages in the BCI system, that is signal acquisition, pre-processing, feature extraction, classification, and applications interface. Fast Fourier Transform (FFT) method used to extract brainwave features. The results of FFT are alpha (8-12Hz) and beta (12-30 Hz) waves in the frequency domain. For classifying brain waves into six classes as input commands to drive a DC motor used Support Vector Machine (SVM) method. Based on the test results, the average accuracy of the classification for the whole class reached 93,1%, the accuracy of class 0 (77,3%), class 1 (95,7%), class 2 (97,8%), class 3 (98,0%), and class 4 (97,5%).

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Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Implementation of Brain Computer Interface (BCI) as a Smart Wheelchair Motion Commands

Yuliana

Riyadi

2022

JBIOMES

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“…The classification process is very useful for analyzing brain pattern characteristics and interpreting EEG signal features represented in a high-dimensional feature space [32]. Numerous machine learning algorithms the BCW literature, including support vector machines (SVMs) [2,14,[33][34][35][36][37][38][39][40][41][42][43][44][45][46], linear discriminant analysis (LDA) [47][48][49][50][51][52][53][54][55][56][57], decision trees (DTs) [5,6,58,59], K-nearest neighbors (KNNs) [60,61], naive bases (NBs) [43,60,62,63], logistic regression (LR) [1,64], and artificial neural networks (ANNs) [1,45,60,61,[64][65][66][67]…”

Section: Introductionmentioning

confidence: 99%

Generalized Time Domain Prediction Model for Motor Imagery-based Wheelchair Movement Control

Al-Qaysi,

Suzani,

Abdul Rashid

et al. 2024

Mesopotamian Journal of Big Data

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Brain-computer interface (BCI-MI)-based wheelchair control is, in principle, an appropriate method for completely paralyzed people with a healthy brain. In a BCI-based wheelchair control system, pattern recognition in terms of preprocessing, feature extraction, and classification plays a significant role in avoiding recognition errors, which can lead to the initiation of the wrong command that will put the user in unsafe condition. Therefore, this research's goal is to create a time-domain generic pattern recognition model (GPRM) of two-class EEG-MI signals for use in a wheelchair control system.This GPRM has the advantage of having a model that is applicable to unknown subjects, not just one. This GPRM has been developed, evaluated, and validated by utilizing two datasets, namely, the BCI Competition IV and the Emotive EPOC datasets. Initially, fifteen-time windows were investigated with seven machine learning methods to determine the optimal time window as well as the best classification method with strong generalizability. Evidently, the experimental results of this study revealed that the duration of the EEG-MI signal in the range of 4–6 seconds (4–6 s) has a high impact on the classification accuracy while extracting the signal features using five statistical methods. Additionally, the results demonstrate a one-second latency after each command cue when using the eight-second EEG-MI signal that the Graz protocol used in this study. This one-second latency is inevitable because it is practically impossible for the subjects to imagine their MI hand movement instantly. Therefore, at least one second is required for subjects to prepare to initiate their motor imagery hand movement. Practically, the five statistical methods are efficient and viable for decoding the EEG-MI signal in the time domain. Evidently, the GPRM model based on the LR classifier showed its ability to achieve an impressive classification accuracy of 90%, which was validated on the Emotive EPOC dataset. The GPRM developed in this study is highly adaptable and recommended for deployment in real-time EEG-MI-based wheelchair control systems.

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“…In another way, EEG testing is also conducted to test the brain stem death (death brain stem), which indicates the presence or absence of the brain's response to stimuli both physically, as well as nonphysical. The utilization of EEG sensors has been applied to virtual simulation [3], and also robots in the assistive technologies [4][5][6][7][8], and also applied to face recognition on smartphones [5]. In this project, the Emotiv Epoc+ EEG sensor [9] is used as input to capture facial expressions will give orders to the Arduino [10] as a controller of the mobile robot used.…”

Section: Introductionmentioning

confidence: 99%

A Study of Mobile Robot Control using EEG Emotiv Epoch Sensor

2018

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Abstract. The study was using an EEG Emotiv Epoc+ sensor to recognize brain activity for controlling a mobile robot's movement. The study used Emotiv Control Panel software for EEG command identification. The commands will be interfaced inside Mind Your OSCs software and processing software which processed inside an Arduino Controller. The output of the Arduino is a movement command (ie. forward, backward, turn left, and turn right). The training methods of the system composed of three sets of thinking mode. First, thinking with doing facial expressions. Second, thinking with visual help. Third, thinking mentally without any help. In the first set, there are two configurations thinking with facial expression help as command of the mobile robot. Final results of the system are the second facial expressions configuration as the best facial expressions method with success rate 88.33 %. The second facial expression configuration has overall response time 1.60175 s faster than the first facial expressions configuration. In these two methods have dominant signals on the frontal lobe. The second facial expressions method have overall respond time 6.12 and 9.53 s faster than thinking with visual, and thinking without help respectively.

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Fast brain control systems for electric wheelchair using support vector machine

Cited by 7 publications

References 8 publications

Implementation of Brain Computer Interface (BCI) as a Smart Wheelchair Motion Commands

Implementation of Brain Computer Interface (BCI) as a Smart Wheelchair Motion Commands

Generalized Time Domain Prediction Model for Motor Imagery-based Wheelchair Movement Control

A Study of Mobile Robot Control using EEG Emotiv Epoch Sensor

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