A Survey on Comparison Analysis Between EEG Signal and MRI for Brain Stroke Detection

Bhattacharjee, Snehashish; Ghatak, Sujata; Dutta, Soumi; Chatterjee, Biswajoy; Gupta, Manash Ranjan

doi:10.1007/978-981-13-1501-5_32

Cited by 12 publications

(5 citation statements)

References 7 publications

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“…Though MEG has a few advantages over EEG due to its higher sensitivity to superficial cortical activity [40], capturing MEG signals requires expensive apparatus and a magnetically shielded room, making it less feasible than EEG. EEG analysis has been used as an approach to successfully detect many other neurological conditions such as Alzheimer's [41,42], Epilepsy [43][44][45][46], Brain Stroke [47], Schizophrenia [48][49][50], Depression [51,52], Insomnia [53], Autism [54] and Attention Deficit Hyperactivity Disorder (ADHD) [55,56]. From the previous studies, it is reasonably evident that EEG captures the minute nuances and salient features of the brain that can lead to the detection of neurological disorders.…”

Section: Related Workmentioning

confidence: 99%

Fractal Dimensions and Machine Learning for Detection of Parkinson’s Disease in Resting-State EEG

Lal,

Chikkankod,

Longo

2023

Preprint

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Parkinson's Disease (PD) is an incurable neurological disorder that degenerates the cerebrospinal nervous system and hinders motor functions. Electroencephalography (EEG) signal analysis can provide reliable information regarding PD conditions. However, EEG is a complex, multi-channel, and non-linear signal with noise that problematizes identifying PD symptoms. A few studies have employed Fractal Dimension (FD) to extract distinguishing PD features from EEG signals. However, no exploratory study exists, as per our knowledge, on the efficiency of the different FD measures. We aim to conduct a comparative analysis of the various FDs that, as feature extraction measures, can discriminate PD patients who are ON and OFF medication from healthy controls using ML architecture. This study has implemented and analyzed several techniques for segmentation, feature extraction, and ML models. The results show that K-Nearest Neighbors (KNN) classifier with Higuchi FD and 90% overlap for segmented window delivers the highest accuracies, yielding a mean accuracy of 99.65% for PD patients ON medication and 99.45% for PD patients OFF medication, respectively. The model accurately identifies the signs of the disease in resting-state EEG with almost equivalent accuracy in both OFF and ON medication patients. To enhance the interpretability in our study, we leveraged XGB's feature importance to generate brain topographic plots. This integration of Explainable AI (XAI) enhanced the transparency and comprehensibility of our model's classifications. Additionally, a comparison between the performance of FD and a few Entropy measures has also been drawn to validate the significance of FD as a superior feature extraction measure. This study contributes to the body of knowledge with an architectural pipeline for detecting PD in resting-state EEG while emphasizing Fractal Dimension as an effective way of extracting salient features from EEG signals.

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Section: Related Workmentioning

confidence: 99%

Fractal Dimensions and Machine Learning for Detection of Parkinson’s Disease in Resting-State EEG

Lal,

Chikkankod,

Longo

2023

Preprint

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“…In this paper, STFT has been used to perform timefrequency analysis of EEG data [28,29]. STFT transforms one dimensional (time domain) EEG data to two dimension (time -frequency).…”

Section: Stft Of Eeg Datamentioning

confidence: 99%

Monitoring of Epileptical Patients Using Cloud-Enabled Health-IoT System

Gupta¹,

Chakraborty²,

Gupta³

2019

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The health Internet of Things (IoT) lays the basis for emergency care for epileptic patients. The security of data transmission in the network calls for a robust monitoring technique. This paper proposes a monitoring model for epileptic patients, using a cloud-based health IoT system. To ensure the data security, watermarking was carried out through discrete wavelet transform-singular value decomposition (DWT-SVD), followed by short time Fourier transform (STFT). The proposed watermarking scheme, which is based on STFT and DWT-SVD, was verified on electroencephalography (EEG) data of class Z and class S. The results show that our scheme achieved a good watermarking performance, with a peak signal-to-noise ratio (PSNR) of 35.25 and a signal-to-noise ratio (SNR) of 31.32.

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“…In general, an EEG records the changes in the electrical potential on the skin surface resulting from the activity of neurons of the cerebral cortex, through electrodes arranged along the scalp [21]. The examination is usually performed for monitoring and diagnosing a number of diseases, although it is being more and more frequently used in biofeedback [22] and in brain-computer interfaces [23], for example, in cognitive state recognition [24,25].…”

Section: Introductionmentioning

confidence: 99%

Detection of Mental Stress through EEG Signal in Virtual Reality Environment

2021

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This paper investigates the use of an electroencephalogram (EEG) signal to classify a subject’s stress level while using virtual reality (VR). For this purpose, we designed an acquisition protocol based on alternating relaxing and stressful scenes in the form of a VR interactive simulation, accompanied by an EEG headset to monitor the subject’s psycho-physical condition. Relaxation scenes were developed based on scenarios created for psychotherapy treatment utilizing bilateral stimulation, while the Stroop test worked as a stressor. The experiment was conducted on a group of 28 healthy adult volunteers (office workers), participating in a VR session. Subjects’ EEG signal was continuously monitored using the EMOTIV EPOC Flex wireless EEG head cap system. After the session, volunteers were asked to re-fill questionnaires regarding the current stress level and mood. Then, we classified the stress level using a convolutional neural network (CNN) and compared the classification performance with conventional machine learning algorithms. The best results were obtained considering all brain waves (96.42%) with a multilayer perceptron (MLP) and Support Vector Machine (SVM) classifiers.

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A Survey on Comparison Analysis Between EEG Signal and MRI for Brain Stroke Detection

Cited by 12 publications

References 7 publications

Fractal Dimensions and Machine Learning for Detection of Parkinson’s Disease in Resting-State EEG

Fractal Dimensions and Machine Learning for Detection of Parkinson’s Disease in Resting-State EEG

Monitoring of Epileptical Patients Using Cloud-Enabled Health-IoT System

Detection of Mental Stress through EEG Signal in Virtual Reality Environment

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