An overview of brain machine interface research in developing countries: Opportunities and challenges

Hassan, Ahnaf Rashik; Huda, Mohammad Nurul; Sarker, Farhana; Mamun, Khondaker A.

doi:10.1109/iciev.2016.7760033

Cited by 7 publications

(19 citation statements)

References 16 publications

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“…Electroencephalography (EEG) is one of the variety of ways that are applied for measuring brain activities. It is usually a non-invasive 160 approach and assesses voltage oscillations ensuing from ionic current inside the brain neurons [12]. Another non-invasive method for measuring brain activ- and inexpensiveness, and they also come with high temporal resolution [18,28].…”

Section: Brain Measurement Techniquesmentioning

confidence: 99%

“…On the other hand, fMRI, MEG, and PET are very costly and not much usable; which makes them unpopular to use [12]. In our experiment, we used Emotiv EPOC+ EEG neuroheadset because it is less sophisticated, inexpensive, and easy to use.…”

Section: Brain Measurement Techniquesmentioning

confidence: 99%

“…This technology has opened a new window of opportunities for the gaming and entertainment industry and different types of games already being developed using this technology to relief from stress [10,11]. 30 Fundamentally, BCI technology can be employed in three ways based on the process of signal acquisition from brain [12]. One way is to place wires inside the grey matter of the brain, this is called invasive BCI [13,3].…”

Section: Introductionmentioning

confidence: 99%

“…One way is to place wires inside the grey matter of the brain, this is called invasive BCI [13,3]. Though invasive BCI begets a better input signal, it is highly sophisticated and extremely sensitive to implement [12,14]. That is why it necessitates very proficient people to 35 manipulate with.…”

Section: Introductionmentioning

confidence: 99%

“…The third type of BCI is called partially invasive BCI. In this technique, 40 wires are placed inside the brain but above the grey matter of it [12,17].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Motor Imagery Tasks from Multi-Channel EEG Data for Brain-Computer Interface Applications

Miah

Rahman

Muhammod

et al. 2020

Preprint

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The classification of motor imagery electroencephalogram (MI-EEG) is a pivotal part of the biosignal classification in the brain-computer interface (BCI) applications. Currently, this bio-engineering based technology is being employed by researchers in various fields to develop cutting edge applications. The classification of real-time MI-EEG signal is the core computing and challenging task in these applications. It is well-known that the existing classification methods are not so accurate due to the high dimensionality and dynamic behaviors of the real-time EEG data. To improve the classification performance of real-time BCI applications, this paper presents a clustering-based ensemble technique and a developed brain game that distinguishes different human thoughts. At first, we have gathered the brain signals, extracted and selected informative features from these signals to generate training and testing sets. After that, we have constructed several classifiers using Artificial Neural Network (ANN), Support Vector Machine (SVM), naïve Bayes, Decision Tree (DT), Random Forest, Bagging, AdaBoost and compared the performance of these existing approaches with suggested clustering-based ensemble technique. On average, the proposed ensemble technique improved the classification accuracy of roughly 5 to 15% compared to the existing methods. Finally, we have developed the targeted * Corresponding author Email address: dewanfarid@cse.uiu.ac.bd (Dewan Md. Farid) brain game employing our suggested ensemble technique. In this game, realtime EEG signal classification and prediction tabulation through animated ball are controlled via threads. By playing this game, users can control the movements of the balls via the brain signals of motor imagery movements without using any traditional input devices. All relevant codes are available via open repository at: https://github.com/mrzResearchArena/MI-EEG. Keywords: Brain Computer Interface (BCI); Brain Machine Interface (BMI); Brain Engineering; Motor Imagery Electroencephalogram (MI-EEG); Motor Cortex; Clustering; Ensemble Learning. 45 are not so accurate due to the high dimensionality and dynamic behaviors of the real-time EEG data [19, 20]. Sometimes, the signals are biased with artifacts and noise due to the low conductivity of the electrodes with the scalp [1]. The objective of this paper is to extend our prior works and ameliorates the classification performance by handling multiple electrodes/ neurons data at the same 50 time. The proposed clustering-based ensemble technique clustered the dataset 3 based on the position of the electrodes so that each cluster represents dissimilar information. It also selects the model dynamically based on the electrode locations to classify real-time EEG data. We have constructed several classifiers using Artificial Neural Network (ANN), Support Vector Machine (SVM), naïve 55Bayes, Decision Tree, Random Forest, Bagging, AdaBoost and compared the performance of these existing approaches with the suggested technique. We also developed a brai...

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Section: Brain Measurement Techniquesmentioning

confidence: 99%

Section: Brain Measurement Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The third type of BCI is called partially invasive BCI. In this technique, 40 wires are placed inside the brain but above the grey matter of it [12,17].…”

Section: Introductionmentioning

confidence: 99%

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