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Trait depressivity prediction with EEG signals via LSBoost
Shenghuan Zhang
, Brendan McCane
, Phoebe S-H Neo
, Shabah M Shadli
, Neil McNaughton
Psychology
University of Otago
Research output
:
Book chapter/Published conference paper
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Conference paper
›
peer-review
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Keyphrases
Depression Diagnosis
25%
Depression Level
25%
Depressive Personality
25%
Depressive State
25%
Depressivity
100%
EEG Biomarkers
50%
EEG Signals
100%
Extreme Score
25%
Feasible Strategy
25%
Fourier Features
25%
G-band
25%
LSBoost
100%
Method Fourier
25%
Personality Inventory for DSM-5 (PID-5)
50%
Psychological Studies
25%
Quantitative Electroencephalography (qEEG)
25%
Recording Channel
25%
Resting-state EEG
25%
Neuroscience
Biological Marker
50%
Depressive Disorder
25%
Electroencephalography
100%
Psychology
Depressive Disorder
25%
Electroencephalography
100%
Resting-State
25%