Artificial Intelligence based Neurodegenerative Disease Diagnosis and Research Analysis using Functional MRI (FMRI): A Review

Nisha, A V; Rajasekaran, M. Pallikonda; Priya, R. Krishna; Bimani, Ali Al

doi:10.1109/icac3n53548.2021.9725691

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…Such information measures the mental states of the users to make these environments safer for human–machine interfaces. The brain’s physiological activities have been studied by electroencephalograms (EEGs) [ 3 ], functional magnetic resonance imaging (fMRI) [ 4 ], functional near-infrared spectroscopy (fNIRS) [ 5 ], magnetoencephalograms (MEGs) [ 6 ], and other forms of biosignals, such as electrooculograms (EOGs) [ 7 ], electrocardiograms (ECGs) [ 7 , 8 ], and galvanic skin responses (GSRs) [ 9 ], to detect various conditions [ 10 , 11 ]. Taking the day-to-day perspective of a mental activity measurement, issues related to size, weight, expense, power consumption, and radioactivity restrict the usage of MEGs and fMRI [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Ensemble Wavelet Decomposition-Based Detection of Mental States Using Electroencephalography Signals

Khare,

Bajaj,

Gaikwad

et al. 2023

Sensors

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Technological advancements in healthcare, production, automobile, and aviation industries have shifted working styles from manual to automatic. This automation requires smart, intellectual, and safe machinery to develop an accurate and efficient brain–computer interface (BCI) system. However, developing such BCI systems requires effective processing and analysis of human physiology. Electroencephalography (EEG) is one such technique that provides a low-cost, portable, non-invasive, and safe solution for BCI systems. However, the non-stationary and nonlinear nature of EEG signals makes it difficult for experts to perform accurate subjective analyses. Hence, there is an urgent need for the development of automatic mental state detection. This paper presents the classification of three mental states using an ensemble of the tunable Q wavelet transform, the multilevel discrete wavelet transform, and the flexible analytic wavelet transform. Various features are extracted from the subbands of EEG signals during focused, unfocused, and drowsy states. Separate and fused features from ensemble decomposition are classified using an optimized ensemble classifier. Our analysis shows that the fusion of features results in a dimensionality reduction. The proposed model obtained the highest accuracies of 92.45% and 97.8% with ten-fold cross-validation and the iterative majority voting technique. The proposed method is suitable for real-time mental state detection to improve BCI systems.

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