EEG Based BCI—Control Applications

Bansal, Dipali; Mahajan, Rashima

doi:10.1016/b978-0-12-814687-3.00006-5

Cited by 10 publications

(11 citation statements)

References 11 publications

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“…2) Digital Filtering 1) Time domain filters: attenuate either very high-or lowfrequency bands while leaving behind the required frequencies [108]. Temporal filters de-noise the EEG and improve its quality by avoiding artifacts caused by interference from power lines and improper polarization of scalp electrodes [109].…”

Section: ) Regression Analysismentioning

confidence: 99%

EEG Signal Processing for Medical Diagnosis, Healthcare, and Monitoring: A Comprehensive Review

Amer,

Belhaouari

2023

IEEE Access

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EEG is a common and safe test that uses small electrodes to record electrical signals from the brain. It has a broad range of applications in medical diagnosis, including diagnosis of epileptic seizure, Alzheimer's, brain tumors, head injury, sleep disorders, stroke, and other seizure and neurological disorders. EEG can also be used to help diagnose death in people who are in a persistent coma. The use of digital signal processing and machine learning to improve EEG analysis for medical diagnosis has gained traction in recent years. This is because EEG visual analysis can be complex and time-consuming, as it mostly involves high dimensions and consists of large datasets. The development of novel sensors for EEG recording, digital signal processing algorithms, feature engineering, and detection algorithms increases the need for efficient diagnostic systems. An extensive review of the recent approaches for EEG preprocessing, extraction of features, and diagnosis of brain disorders is provided. In this paper, the main focus is to identify reliable algorithms for preprocessing, feature engineering, and classification of EEG, applied to medical healthcare and diagnosis, providing practitioners with insights into the most effective strategies, as well as potential future directions for improving accuracy of the automatic diagnostic systems. The study of reliable feature extraction and classification algorithms is crucial for a more accurate analysis of EEG signals. This paper can provide valuable information to researchers and practitioners working in the fields of EEG analysis and machine learning, as it provides a summary of recent developments and highlights key areas for future research. This paper can help researchers and clinicians to stay up-to-date on the latest developments in this field.

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Section: ) Regression Analysismentioning

confidence: 99%

EEG Signal Processing for Medical Diagnosis, Healthcare, and Monitoring: A Comprehensive Review

Amer,

Belhaouari

2023

IEEE Access

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“…P300 obtained from parietal and central areas as a response to visual or hearing salient stimuli ( 15 )…”

Section: Pain and Attentionmentioning

confidence: 99%

Association of Pain During the Evaluation of Delirium in Intensive Care Unit Patients

Álvarez

Parada

2021

Front. Med.

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“…Short sessions and refreshments during breaks should be available for the subject to stay concentrated during data acquisition. Also, the subject should be told about objects and the importance of obtaining high-quality data [43].…”

Section: Pre-processingmentioning

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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EEG Based BCI—Control Applications

Cited by 10 publications

References 11 publications

EEG Signal Processing for Medical Diagnosis, Healthcare, and Monitoring: A Comprehensive Review

EEG Signal Processing for Medical Diagnosis, Healthcare, and Monitoring: A Comprehensive Review

Association of Pain During the Evaluation of Delirium in Intensive Care Unit Patients

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

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