Brain-Related Diseases and Role of Electroencephalography (EEG) in Diagnosing Brain Disorders

Dhar, Puja; Garg, Vijay Kumar

doi:10.1007/978-981-15-8354-4_32

“…In patients with head injury who have fallen into a coma, EEG is of paramount importance in the assessment of brain responsiveness and the quantification of damage. It may be used in the determination of early abnormal loci within the brain, loci which may be reflecting the onset of ailments such as epilepsy, dementia, and Alzheimer's [5,6].…”

Section: Representatives Examples In Realistic Cases In Diagnosing Br...mentioning

confidence: 99%

A Novel Battery-Supplied AFE EEG Circuit Capable of Muscle Movement Artifact Suppression

Delis,

Tsavdaridis,

Tsanakas

2024

Preprint

0

View full text Add to dashboard Cite

In this study, the fundamentals of electroencephalography signals, their categorization into frequency sub-bands, the circuitry used for their acquisition, and the impact of noise interference on signal acquisition are examined. Additionally, design specifications for medical-grade and research-grade EEG circuits and a comprehensive analysis of various analog front-end architectures for electroencephalograph (EEG) circuit design are presented. Three distinct selected case studies are examined in terms of comparative evaluation with generic EEG circuit design templates. Moreover, a novel one-channel battery-supplied EEG analog front-end circuit designed to address the requirements of usage protocols containing strong com-pound muscle movements is introduced. Furthermore, a realistic input signal generator circuit is proposed that models the human body and the Electromagnetic Interference from its surroundings. Experimental simulations are conducted in 50 Hz and 60 Hz electrical grid environments to evaluate the performance of the novel design. The results demonstrate the efficacy of the proposed system, particularly in terms of bandwidth, portability, common mode rejection ratio , gain, suppression of muscle movement artifacts, electrostatic discharge and leakage current protection. Conclusively, the novel design is cost-effective and suitable for both commercial and research single-channel EEG applications. It can be easily incorporated in Brain Computer Interfaces and neurofeedback training systems.

show abstract

“…In patients with head injury who have fallen into a coma, EEG is of paramount importance in the assessment of brain responsiveness and the quantification of damage [9]. It may be used in the determination of early abnormal loci within the brain, loci which may be reflecting the onset of ailments such as epilepsy, dementia, and Alzheimer's [10,11].…”

Section: Medical Significance Of Eeg Signals Acquisitionmentioning

confidence: 99%

A Novel Battery-Supplied AFE EEG Circuit Capable of Muscle Movement Artifact Suppression

Delis,

Tsavdaridis,

Tsanakas

2024

Preprint

0

View full text Add to dashboard Cite

In this study, the fundamentals of electroencephalography signals, their categorization into frequency sub-bands, the circuitry used for their acquisition, and the impact of noise interference on signal acquisition are examined. Additionally, design specifications for medical-grade and research-grade EEG circuits and a comprehensive analysis of various analog front-end architectures for electroencephalograph (EEG) circuit design are presented. Three distinct selected case studies are examined in terms of comparative evaluation with generic EEG circuit design templates. Moreover, a novel one-channel battery-supplied EEG analog front-end circuit designed to address the requirements of usage protocols containing strong com-pound muscle movements is introduced. Furthermore, a realistic input signal generator circuit is proposed that models the human body and the Electromagnetic Interference from its surroundings. Experimental simulations are conducted in 50 Hz and 60 Hz electrical grid environments to evaluate the performance of the novel design. The results demonstrate the efficacy of the proposed system, particularly in terms of bandwidth, portability, common mode rejection ratio , gain, suppression of muscle movement artifacts, electrostatic discharge and leakage current protection. Conclusively, the novel design is cost-effective and suitable for both commercial and research single-channel EEG applications. It can be easily incorporated in Brain Computer Interfaces and neurofeedback training systems.

show abstract

“…Electroencephalography (EEG) is one type of brain imaging technique and has been widely used to monitor and diagnose brain status of patients with various brain diseases (e.g., epilepsy) [2,[10][11][12]16]. EEG data typically consists of multiple sequences (or channels) of waveform signals, with each sequence obtained by densely sampling electrical signals of brain activities from a unique electrode attached to a specific position on patient's head surface.…”

Section: Introductionmentioning

confidence: 99%

Task-oriented Self-supervised Learning for Anomaly Detection in Electroencephalography

Zheng¹,

Liu²,

Mo³

et al. 2022

Preprint

0

View full text Add to dashboard Cite

Accurate automated analysis of electroencephalography (EEG) would largely help clinicians effectively monitor and diagnose patients with various brain diseases. Compared to supervised learning with labelled disease EEG data which can train a model to analyze specific diseases but would fail to monitor previously unseen statuses, anomaly detection based on only normal EEGs can detect any potential anomaly in new EEGs. Different from existing anomaly detection strategies which do not consider any property of unavailable abnormal data during model development, a task-oriented self-supervised learning approach is proposed here which makes use of available normal EEGs and expert knowledge about abnormal EEGs to train a more effective feature extractor for the subsequent development of anomaly detector. In addition, a specific two-branch convolutional neural network with larger kernels is designed as the feature extractor such that it can more easily extract both largerscale and small-scale features which often appear in unavailable abnormal EEGs. The effectively designed and trained feature extractor has shown to be able to extract better feature representations from EEGs for development of anomaly detector based on normal data and future anomaly detection for new EEGs, as demonstrated on three EEG datasets. The code is available at https://github.com/ironing/EEG-AD.

show abstract

Brain-Related Diseases and Role of Electroencephalography (EEG) in Diagnosing Brain Disorders

Cited by 5 publications

References 19 publications

A Novel Battery-Supplied AFE EEG Circuit Capable of Muscle Movement Artifact Suppression

A Novel Battery-Supplied AFE EEG Circuit Capable of Muscle Movement Artifact Suppression

A Novel Battery-Supplied AFE EEG Circuit Capable of Muscle Movement Artifact Suppression

Task-oriented Self-supervised Learning for Anomaly Detection in Electroencephalography

Contact Info

Product

Resources

About