Basics of Brain Computer Interface

Ramadan, Rabie A.; Refat, Samah; Elshahed, Marwa A.; Ali, Rasha A.

doi:10.1007/978-3-319-10978-7_2

Cited by 56 publications

(44 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The main advantage of this technique is that the signal acquired is of the highest quality as the signal is recorded directly from inside of the brain. This technique has one disadvantage that a critical surgery needs to be performed for placing the electrodes deep inside the brain [2]. (2) Partially Invasive BCI Acquisition Technique Another technique in which the electrodes are placed inside the skull but over the surface of the brain is partially invasive method [2].…”

Section: Classificationmentioning

confidence: 99%

“…This technique has one disadvantage that a critical surgery needs to be performed for placing the electrodes deep inside the brain [2]. (2) Partially Invasive BCI Acquisition Technique Another technique in which the electrodes are placed inside the skull but over the surface of the brain is partially invasive method [2]. The strength of the signal acquired for this method is weaker than those acquired using the invasive method [3].…”

Section: Classificationmentioning

confidence: 99%

“…BCI signal processing [2] Signal processing The signals acquired from the surface of the brain needs to be processed. The steps are discussed below : Pre-processing Acquired signal from the brain suffers from a lot of noise.…”

mentioning

confidence: 99%

“…This noise has to be removed in order to prepare our signal for further processing which makes our signal more efficient. [2].…”

mentioning

confidence: 99%

See 3 more Smart Citations

BCI based control of wheelchair for mobility

kohli¹,

Warhade²

2017

IJET

View full text Add to dashboard Cite

Abstract-Brain Computer Interface(BCI) is a platform that provides an opportunity to develop an alternative way of communication for patients who lose their ability to communicate. BCI helps people restore their mobility which is driving a wheelchair in this case. With the help of this technique one can understand the intended thoughts of such users. In this paper, different signals from such patients have been analyzed. Essential steps like pre-processing, feature extraction and classification are performed that helps us in analyzing the signal.

show abstract

Section: Classificationmentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

mentioning

confidence: 99%

“…This noise has to be removed in order to prepare our signal for further processing which makes our signal more efficient. [2].…”

mentioning

confidence: 99%

See 2 more Smart Citations

BCI based control of wheelchair for mobility

kohli¹,

Warhade²

2017

IJET

View full text Add to dashboard Cite

show abstract

“…Non-invasive methods do not need surgery or insertion of an instrument in the patient's body. These methods include models such as Electroencephalography (EEG) EEG recordings are measured in Hertz (cycles per second) and are divided into different frequency bands as slow, moderate, and fast waves [11] as shown in Table I. To record brain activity, a complete paradigm needs to be designed.…”

Section: Introductionmentioning

confidence: 99%

A Review and Classification of Widely used Offline Brain Datasets

Wasim¹,

Sajjad²,

Ramzan³

et al. 2018

ijacsa

View full text Add to dashboard Cite

Abstract-Brain Computer Interfaces (BCI) are a natural extension to Human Computer Interaction (HCI) technologies. BCI is especially useful for people suffering from diseases, such as Amyotrophic Lateral Sclerosis (ALS) which cause motor disabilities in patients. To evaluate the effectiveness of BCI in different paradigms, the need of benchmark BCI datasets is increasing rapidly. Although, such datasets do exist, a comparative study of such datasets is not available to the best of our knowledge. In this paper, we provided a comprehensive overview of various BCI datasets. We briefly describe the characteristics of these datasets and devise a classification scheme for them. The comparative study provides feature extractors and classifiers used for each dataset. Moreover, potential use-cases for each dataset are also provided.

show abstract

A Proposed LSTM‐Based Neuromarketing Model for Consumer Emotional State Evaluation Using EEG

Gill

Singh

2022

Advanced Analytics and Deep Learning Models

View full text Add to dashboard Cite

Basics of Brain Computer Interface

Cited by 56 publications

References 22 publications

BCI based control of wheelchair for mobility

BCI based control of wheelchair for mobility

A Review and Classification of Widely used Offline Brain Datasets

A Proposed LSTM‐Based Neuromarketing Model for Consumer Emotional State Evaluation Using EEG

Contact Info

Product

Resources

About