Brain-Computer Interface Learning System for Quadriplegics

Kanagasabai, P. Sastha; Gautam, Ritesh; Rathna, G. N.

doi:10.1109/mite.2016.058

Cited by 8 publications

(8 citation statements)

References 8 publications

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“…The details of each research area and the equipment from different companies are: Education: Only eight articles were found in this area with two companies. Five articles used Emotiv equipment [36][37][38][39][40] and another three articles used the equipment from InteraXon company [41][42][43].…”

Section: Rq1: What Are the Publication Trends Based On Eeg Equipment?mentioning

confidence: 99%

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: Rq1: What Are the Publication Trends Based On Eeg Equipment?mentioning

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 rely on the weak Electroencephalogram (EEG) signals that arise from the neural activities and are measured using non-invasive electrodes suitably placed on the skull's surface. Introduced by Vidal in 1973 [1], BCI is currently an active research direction with applications in diverse areas including intelligent home control [2], speech synthesis [3], spelling applications [4], readiness detection [5], Epilepsy Prognosis [6], wheelchair control [7], microsleep prevention [8], limb rehabilitation [9], mobile robots [10,11], drowsiness control [12], and assistive systems for people with severe handicaps enabling them, for example, to control electronic devices [13] or browse the internet [14] .…”

Section: Introductionmentioning

confidence: 99%

“…Deliberate rapid and successive eye blinks, on the other hand, are used as synchronization signals to start/stop the system, initiate and confirm a command, and if sustained, call for emergency. The aforementioned dual use of eye blinks is original in this work compared to other assistive systems [13,14] and constitutes one of the smart features of the proposed BCI.…”

Section: Introductionmentioning

confidence: 99%

“…Instead of imposing this restriction in our work, the user is only expected to rapidly blink (without specifying the speed as long as it is faster than the regular blinking rate of around 1 Hz) every time synchronization is required to control the system operation. In the context of relevant publications [13][14][15], this paper offers the following contributions: The remainder of this paper is organized as follows. In section 2, the data collection stage of the work is summarized.…”

Section: Introductionmentioning

confidence: 99%

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Versatile Brain-Computer-Interface for Severely -Disabled People

Masaad¹,

Jassim²,

Mahdi³

et al. 2021

IJCDS

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“…Human-machine communication using brainwaves (Brain Computer Interface: BCI) [5] can be applied to help people with disability (PWD). PWD can operate and control computer equipment or appliances through brainwaves detection.…”

Section: Introductionmentioning

confidence: 99%

Brainwave Detection Model for Panic Attacks Based on Event-related Potential

Sirikongtham¹,

Paireekreng²,

Chit³

2019

JUBPAS

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Panic attacks could adversely affect a patient’s daily life and can pose risks to others. The symptoms of panic attacks can be timely observed by detecting the brainwave. This research presents a model that can evaluate the level of panic attack symptoms using the brainwaves detection during (or before) the symptom occurs. It helps monitor the patient’s brainwave based on Event-related potential (ERP). The model is derived from the simulation with horror pictures and frightening sound on the experimental group of 30 people. The survey related to symptoms has been used regarding to the criteria of the Beck Anxiety Inventory (BAI). The results showed that there is a consistent change of Electroencephalography (EEG) in each change of brainwaves where its quantitative analysis found that the changes of Beta, Gamma, and Alpha directly affect the model of Brainwaves Panic Attacks Measurement (BPAM) which is associated with panic attacks. 1 out of 30 cases scored higher than the average of the BPAM at 220 The Model BPAM can detect the risk to be Panic Attack compared to the use of tests Beck Anxiety Inventory (BAI) were found to be consistent. The test value BAI Score 19-63 was BPAM Score 401-1000. In addition, the results found that at P300 the brainwave pattern of EEG in meditation had decreased significantly whereas the brainwave related to attention had increased considerably for which human brain can potentially respond to stimulated external events.

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Brain-Computer Interface Learning System for Quadriplegics

Cited by 8 publications

References 8 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

Versatile Brain-Computer-Interface for Severely -Disabled People

Brainwave Detection Model for Panic Attacks Based on Event-related Potential

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