Brain computer interface based assistive device

Ramesh, C R; Das, Lyla B

doi:10.1109/icacci.2015.7275630

Cited by 4 publications

(4 citation statements)

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“…Mr. Ramesh C R and Prof. Lyla B Das, 2015 achieved 81.17% accuracy and also when the device is active and taking command, it goes to attention mode and for idle time, it goes for mediation mode. Also Computing et al, 2015 went through a proper FFT algorithm analysis [4] that was not mentioned in [1,2,3].…”

Section: Review Based On Experimental Resultsmentioning

confidence: 99%

“…When it comes to the results, systems developed using FFT are suited for physically disabled persons but lacks the communication speed [2]. Using ERP, the accuracy rate can be increased but it would need the users to be trained [3,10]. Even though ANN is better for users, researchers have found only 70% accuracy when users are concentrating [12].…”

Section: Observation and Discussionmentioning

confidence: 99%

“…Only sufficiently strong potentials generated synchronically by open fields are recordable across the scalp Non-invasive BCI [3,6,8,13,17,19] Very fast Least signal clarity P-300 [15,16] Requires no user training…”

Section: Review Based On Methodsmentioning

confidence: 99%

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A Review based on Brain Computer Interaction using EEG Headset for Physically Handicapped People

Saif¹,

Hossain²,

Ahmed³

et al. 2019

IJEME

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The universes' most complex structure is the human brain. To analyze its characteristics, many studies and experiments have been carried out in a proper and systematic manner. From these researches and experiments, scientists have learnt to communicate with computer using brain and hence, BCI has been developed. A Brain-Computer Interface (BCI) provides a communication path between the human brain and the computer system. With the advancement of information technology and neuroscience, there has been a flow of interest in turning fiction into reality. This research investigated existing works of BCI with the purpose developing a system that allows physically challenged people to communicate with other persons and helps to interact with the external environments with the help of computers. Components like, comparison of invasive and non-invasive technologies to measure brain activity, evaluation of control signals (i.e. patterns of brain activity or brain waves that can be used for communication), development or improvement of algorithms for translation of brain signals into computer commands, specific frequency components like electroencephalography (EEG), artificial neural network (ANN) etc. are used to accomplish such a feat. With such, the developments of new BCI applications are emerging every day.

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Section: Review Based On Experimental Resultsmentioning

confidence: 99%

Section: Observation and Discussionmentioning

confidence: 99%

See 1 more Smart Citation

A Review based on Brain Computer Interaction using EEG Headset for Physically Handicapped People

Saif¹,

Hossain²,

Ahmed³

et al. 2019

IJEME

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“…Since then, this technology has evolved considerably and nowadays the main applications are found in clinical environments. Areas, such as neuro-rehabilitation for patients with neurodegenerative diseases [ 2 , 3 , 4 , 5 , 6 ], as well as assistive technologies for people with motor impairments [ 7 , 8 , 9 , 10 ], have had the widest presence. However, BCI’s applications have now transcended the clinical environment [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Single-Option P300-BCI Performance Is Affected by Visual Stimulation Conditions

Peguero

Mendoza-Montoya

Antelis

2020

Sensors

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The P300 paradigm is one of the most promising techniques for its robustness and reliability in Brain-Computer Interface (BCI) applications, but it is not exempt from shortcomings. The present work studied single-trial classification effectiveness in distinguishing between target and non-target responses considering two conditions of visual stimulation and the variation of the number of symbols presented to the user in a single-option visual frame. In addition, we also investigated the relationship between the classification results of target and non-target events when training and testing the machine-learning model with datasets containing different stimulation conditions and different number of symbols. To this end, we designed a P300 experimental protocol considering, as conditions of stimulation: the color highlighting or the superimposing of a cartoon face and from four to nine options. These experiments were carried out with 19 healthy subjects in 3 sessions. The results showed that the Event-Related Potentials (ERP) responses and the classification accuracy are stronger with cartoon faces as stimulus type and similar irrespective of the amount of options. In addition, the classification performance is reduced when using datasets with different type of stimulus, but it is similar when using datasets with different the number of symbols. These results have a special connotation for the design of systems, in which it is intended to elicit higher levels of evoked potentials and, at the same time, optimize training time.

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