Literature review of brain computer interface (BCI) using Electroencephalogram signal

Shende, Pravin M; Jabade, Vaishali

doi:10.1109/pervasive.2015.7087109

Cited by 18 publications

(4 citation statements)

References 16 publications

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“…Researchers have started using BCI technology in various applications [1], [86] and show below in Figure 9. Some of the applications where it is used now a day are as follows:…”

Section: Applications Of Bcimentioning

confidence: 99%

A Survey in Implementation and Applications of Electroencephalograph (EEG)-Based Brain-Computer Interface

Abdulwahab

Khleaf²,

Jasim³

2021

ETJ

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A Brain-Computer Interface (BCI) is an external system that controls activities and processes in the physical world based on brain signals. In Passive BCI, artificial signals are automatically generated by a computer program without any input from nerves in the body. This is useful for individuals with mobility issues. Traditional BCI has been dependent only on recording brain signals with Electroencephalograph (EEG) and has used a rule-based translation algorithm to generate control commands. These systems have developed very accurate translation systems. This paper is about the different methods for adapting the signals from the brain. It has been mentioned that various kinds of surveys in the past to serve the purpose of the present research. This paper shows a simple and easy analysis of each technique and its respective benefits and drawbacks, including signal acquisition, signal pre-processing, feature classification and classification. Finally, discussed is the application of EEG-based BCI.

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“…Researchers have started using BCI technology in various applications [1], [86] and show below in Figure 9. Some of the applications where it is used now a day are as follows:…”

Section: Applications Of Bcimentioning

confidence: 99%

A Survey in Implementation and Applications of Electroencephalograph (EEG)-Based Brain-Computer Interface

Abdulwahab

Khleaf²,

Jasim³

2021

ETJ

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“…Education, production, marketing, security, and entertainment are just a few of the fields where brain signal reading and remote communication have made an influence [3]- [5]. Figure 1 illustrates the comparison between various non-invasive and invasive brain computer interface (BCI) technology that is presently available [6], [7] where in the latter, an IC is surgically placed inside the brain, which is why non-invasive BCI, which just requires the use of a headset or a cap with active electrode system, is preferred [8]. EEG became the popular and preferable non-invasive BCI method because it is cheap, simple, quick response, and its ability to be implemented with portable products [9], [10].…”

Section: Literature Reviewmentioning

confidence: 99%

Electrical Appliance Switching Controller by Brain Wave Spectrum Evaluation Using a Wireless EEG Headset

F.¹,

Miskon²,

Hamzah³

et al. 2021

IJETAE

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Disabled people are usually unable to interact with their surroundings efficiently, and performing tasks like switching an appliance on or off can be troublesome if the user is bedridden, for example. This article discusses an electrical appliance switching controller using a wireless EEG headset that is aimed to aid elderly people and the disabled. The system comprises of a MindLink EEG headset that is Bluetooth-connected to an Arduino microcontroller board. The system permits the user to separately switch on and off the 4 electrical devices connected to the power socket. The EEG signal is obtained to investigate the brain activity throughout the experiments done. Based on the brain wave signals read, attention and meditation are determined to be the most suitable for this project and is used to trigger the relay switching of the power socket. It is found that the response time to trigger the switching is slow as some users require practice or training to control their brain wave signals effectively. The work performed provides a rudimentary insight of a BCI system functionalities and presents a brainwave-controlled hardware switching for the bedridden or disabled patients.

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“…Non-invasive BCI systems use electrodes distributed over specific areas of the scalp [1]. These electrodes are either dry or wet [2]. The recorded signals are classified depending on the biophysical nature of the signal source [1]: Metabolic measured by techniques like (near-infrared spectroscopy (NIRS), Electrophysiological that measured by electroencephalography (EEG), Magnetic signal that measured by magnetoencephalography (MEG) [3] and [4].…”

Section: Introductionmentioning

confidence: 99%

Wheelchair Free Hands Navigation Using Robust DWT_AR Features Extraction Method With Muscle Brain Signals

2020

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Researchers try to help disabled people by introducing some innovative applications to support and assess their life. The Brain-Computer Interface (BCI) application that covers both hardware and software models, is considered in this work. BCI is implemented based on brain signals to be converted to commands. To increase the number of commands, non-brain source signals are used, such as eye-blinking, teeth clenching, jaw squeezing, and other movements. This paper introduced a low dimensions robust method to detect the eye-blinks and jaw squeezing; so that the method can be applied to drive a wheelchair by using five commands. Our approach is used Discrete Wavelet Transform with Autoregressive to extract the signal's features. These features are classified by using a linear Support Vector Machine (SVM) classifier. The present method detects every testing sample using a small training set to test and drive a powered wheelchair. The proposed method is fully implemented practically based on binary-coded commands.

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Literature review of brain computer interface (BCI) using Electroencephalogram signal

Cited by 18 publications

References 16 publications

A Survey in Implementation and Applications of Electroencephalograph (EEG)-Based Brain-Computer Interface

A Survey in Implementation and Applications of Electroencephalograph (EEG)-Based Brain-Computer Interface

Electrical Appliance Switching Controller by Brain Wave Spectrum Evaluation Using a Wireless EEG Headset

Wheelchair Free Hands Navigation Using Robust DWT_AR Features Extraction Method With Muscle Brain Signals

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