Towards affirmation of recovery of deeply embedded autobiographical memory and identification of an EEG biomarker using wearable sensors

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Towards affirmation of recovery of deeply embedded autobiographical memory and identification of an EEG biomarker using wearable sensors

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2022-06

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The importance of background music in memory retrieval is irrefutable. Music can boost brain activity and jog deeply embedded memories. This is why background music is used as a popular therapy for dementia patients. Previous studies used music to recall lyrics, serial of words, and long and short-term memories. In this research, EEG and EDA data were collected from 40 healthy participants using wearable sensors during 9 music sessions (3 happy, 3 sad, and 3 neutral). A post-study survey was given to all participants after each piece of music to know if they recalled any autobiographical memories. The main objective is to find an EEG biomarker using the collected qualitative and quantitative data. The study discovered that during the memory "recall" scenario, alpha power (F3: p = 0.0066, F7: p = 0.0386, F4: p = 0.0023, and F8: p = 0288) increases significantly (on average 16.2%) compared to the "no-recall" scenario for all 4 EEG channels. We interpret increased alpha power (8–12 Hz) as a biomarker for autobiographical memory recall. In addition, for the EDA signal, there was a significant difference for the Tonic Standard Deviation (p = 0.0171), Tonic Min (p = 0.0092), Phasic Standard Deviation (p = 0.0260), Phasic Max (p = 0.0011), and Phasic Energy (p = 0.0478).

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University of Minnesota M.S. thesis. 2022. Major: Computer Science. Advisor: Arshia Khan. 1 computer file (PDF); 97 pages.

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Das, Rupak Kumar. (2022). Towards affirmation of recovery of deeply embedded autobiographical memory and identification of an EEG biomarker using wearable sensors. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/241690.

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