Investigating the Possibility of Brain Actuated Mobile Robot Through Single-Channel EEG Headset

Rashid, Mamunur; Sulaiman, Norizam; Mustafa, Mahfuzah; Khatun, Sabira; Bari, Bifta Sama; Hasan, Jahid; Al-Fakih, Nawfan Mohammed Mohammed Ahmed

doi:10.1007/978-981-15-2317-5_49

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…Engineering : This is the second highest research area in this SLR, with 24 articles and 9 companies. Eight articles used Emotiv equipment [ 5 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ], five articles used g.Tec equipment [ 51 , 52 , 53 , 54 , 55 ], three articles used Brain Products equipment [ 56 , 57 , 58 ], two articles used Compumedics Neuroscan equipment [ 59 , 60 ], and two articles used NeuroSky equipment [ 61 , 62 ]. For the equipment for Cognionics Inc. [ 63 ], Electrical Geodesics Inc. [ 64 ], Neuroelectrics [ 65 ], and Advanced Brain Monitoring [ 66 ], one article each used their equipment.…”

Section: Resultsmentioning

confidence: 99%

“…Furthermore, games can help in rehabilitation and people can enjoy them and feel entertained. Vega et al [ 70 ] proposed a game for controlling video games using prosthetic devices and Rashid et al [ 61 ] proposed a game for increased attention, particularly for people with attention deficit disorder. However, the challenge in implementing BCIs in gaming can be the user experience.…”

Section: Discussionmentioning

confidence: 99%

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Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review

Jamil

Belkacem

Ouhbi

et al. 2021

Sensors

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Humans interact with computers through various devices. Such interactions may not require any physical movement, thus aiding people with severe motor disabilities in communicating with external devices. The brain–computer interface (BCI) has turned into a field involving new elements for assistive and rehabilitative technologies. This systematic literature review (SLR) aims to help BCI investigator and investors to decide which devices to select or which studies to support based on the current market examination. This examination of noninvasive EEG devices is based on published BCI studies in different research areas. In this SLR, the research area of noninvasive BCIs using electroencephalography (EEG) was analyzed by examining the types of equipment used for assistive, adaptive, and rehabilitative BCIs. For this SLR, candidate studies were selected from the IEEE digital library, PubMed, Scopus, and ScienceDirect. The inclusion criteria (IC) were limited to studies focusing on applications and devices of the BCI technology. The data used herein were selected using IC and exclusion criteria to ensure quality assessment. The selected articles were divided into four main research areas: education, engineering, entertainment, and medicine. Overall, 238 papers were selected based on IC. Moreover, 28 companies were identified that developed wired and wireless equipment as means of BCI assistive technology. The findings of this review indicate that the implications of using BCIs for assistive, adaptive, and rehabilitative technologies are encouraging for people with severe motor disabilities and healthy people. With an increasing number of healthy people using BCIs, other research areas, such as the motivation of players when participating in games or the security of soldiers when observing certain areas, can be studied and collaborated using the BCI technology. However, such BCI systems must be simple (wearable), convenient (sensor fabrics and self-adjusting abilities), and inexpensive.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review

Jamil

Belkacem

Ouhbi

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…These values have been computed using Eq. (7)(8)(9)(10)(11). The performance of the proposed work is shown in Table 2.…”

Section: Resultsmentioning

confidence: 99%

“…In this study, we analyze the FFT, PSD, Spectral centroids, the standard deviation to achieve the best feature for analysis of the dataset [11].…”

Section: Preprocessing and Feature Extractionmentioning

confidence: 99%

Auditory Evoked Potentials (AEPs) Response Classification: A Fast Fourier Transform (FFT) and Support Vector Machine (SVM) Approach

Islam¹,

Sulaiman²,

Rashid³

et al. 2021

Lecture Notes in Electrical Engineering

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Hearing loss has become the world's most widespread sensory impairment. The applicability of a traditional hearing test is limited as it allows the subject to provide a direct response. The main aim of this study is to build an intelligent hearing level evaluation method using possible auditory evoked signals (AEPs). AEP responses are subjected to fixed acoustic stimulation strength for usual auditory and abnormal ear subjects to detect the hearing disorder. In this paper, the AEP responses have been captured from the sixteen subjects when the subject hears the auditory stimulus in the left or right ear. Then, the features have extracted with the help of Fast Fourier Transform (FFT), Power Spectral Density (PSD), Spectral Centroids, Standard Deviation algorithms. To classify the extracted features, the Support Vector Machine (SVM) approach using Radial Basis Kernel Function (RBF) has been used. Finally, the performance of the classifier in terms of accuracy, confusion matrix, true positive and false negative rate, precision, recall, and Cohen-Kappa-Score have been evaluated. The maximum classification accuracy of the developed SVM model with FFT feature was observed 95.29% (10 s time windows) which clearly indicates that the method provides a very encouraging performance for detecting the AEPs responses.

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Development of EEG-Based System to Identify Student Learning Attention Ability

Sulaiman

Ismail

Islam

et al. 2021

Lecture Notes in Electrical Engineering

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Investigating the Possibility of Brain Actuated Mobile Robot Through Single-Channel EEG Headset

Cited by 5 publications

References 16 publications

Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review

Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review

Auditory Evoked Potentials (AEPs) Response Classification: A Fast Fourier Transform (FFT) and Support Vector Machine (SVM) Approach

Development of EEG-Based System to Identify Student Learning Attention Ability

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