Using deep learning to decode abnormal brain neural activity in MDD from single-trial EEG signals

Liu, Mengmeng; Tan, Jianling; Jiang, Yuhao; Tian, Yin

doi:10.1080/27706710.2022.2075242

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…Classifying ALS on a single-trial basis involves training a machine learning model with multiple samples/trials of a quantifiable objective marker that can efficiently predict a sample/trial as ALS or healthy after proper training. Single-trial detection using machine learning has shown great potential in several neural disorders including major depressive disorder (MDD) ( Liu et al, 2022 ), autism spectrum disorder (ASD) ( Ezabadi and Moradi, 2021 ), post-traumatic stress disorder (PTSD) ( Georgopoulos et al, 2010 ), schizophrenia ( Xu et al, 2013 ), amongst other neurologic disorders ( Aoe et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Automatic detection of ALS from single-trial MEG signals during speech tasks: a pilot study

Dash,

Teplansky,

Ferrari

et al. 2024

Front. Psychol.

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Amyotrophic lateral sclerosis (ALS) is an idiopathic, fatal, and fast-progressive neurodegenerative disease characterized by the degeneration of motor neurons. ALS patients often experience an initial misdiagnosis or a diagnostic delay due to the current unavailability of an efficient biomarker. Since impaired speech is typical in ALS, we hypothesized that functional differences between healthy and ALS participants during speech tasks can be explained by cortical pattern changes, thereby leading to the identification of a neural biomarker for ALS. In this pilot study, we collected magnetoencephalography (MEG) recordings from three early-diagnosed patients with ALS and three healthy controls during imagined (covert) and overt speech tasks. First, we computed sensor correlations, which showed greater correlations for speakers with ALS than healthy controls. Second, we compared the power of the MEG signals in canonical bands between the two groups, which showed greater dissimilarity in the beta band for ALS participants. Third, we assessed differences in functional connectivity, which showed greater beta band connectivity for ALS than healthy controls. Finally, we performed single-trial classification, which resulted in highest performance with beta band features (∼ 98%). These findings were consistent across trials, phrases, and participants for both imagined and overt speech tasks. Our preliminary results indicate that speech-evoked beta oscillations could be a potential neural biomarker for diagnosing ALS. To our knowledge, this is the first demonstration of the detection of ALS from single-trial neural signals.

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Section: Introductionmentioning

confidence: 99%

Automatic detection of ALS from single-trial MEG signals during speech tasks: a pilot study

Dash,

Teplansky,

Ferrari

et al. 2024

Front. Psychol.

View full text Add to dashboard Cite

show abstract

“…Classifying ALS on a single-trial basis involves training a machine learning model with multiple samples/trials of a quantifiable objective marker that can efficiently predict a sample/trial as ALS or healthy after proper training. Single-trial detection using machine learning has shown great potential in several neural disorders including major depressive disorder (MDD) (Liu et al, 2022), autism spectrum disorder (ASD) (Ezabadi and Moradi, 2021), post-traumatic stress disorder (PTSD) (Georgopoulos et al, 2010), schizophrenia (Xu et al, 2013), amongst other neurologic disorders (Aoe et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Data_Sheet_1.docx

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Privacy Preserving Collaboratively Training Framework for Classification of Major Depressive Disorder using Non-IID Three Channel Electroencephalogram

Gupta,

Khullar

2024

Procedia Computer Science

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Using deep learning to decode abnormal brain neural activity in MDD from single-trial EEG signals

Cited by 4 publications

References 25 publications

Automatic detection of ALS from single-trial MEG signals during speech tasks: a pilot study

Automatic detection of ALS from single-trial MEG signals during speech tasks: a pilot study

Data_Sheet_1.docx

Privacy Preserving Collaboratively Training Framework for Classification of Major Depressive Disorder using Non-IID Three Channel Electroencephalogram

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