ANet: Autoencoder-Based Local Field Potential Feature Extractor for Evaluating An Antidepressant Effect in Mice after Administering Kratom Leaf Extracts

no code implementations • 17 Sep 2022 • Jakkrit Nukitram, Rattanaphon Chaisaen, Phairot Autthasan, Narumon Sengnon, Juraithip Wungsintaweekul, Wanumaidah Saengmolee, Dania Cheaha, Ekkasit Kumarnsit, Thapanun Sudhawiyangkul, Theerawit Wilaiprasitporn

Here, we adopted an autoencoder (AE)-based anomaly detector called ANet to measure the similarity of mice's local field potential (LFP) features that responded to KT leave extracts and AD flu.

A Pilot Study on Visually Stimulated Cognitive Tasks for EEG-Based Dementia Recognition

no code implementations • 5 Mar 2021 • Supavit Kongwudhikunakorn, Suktipol Kiatthaveephong, Kamonwan Thanontip, Pitshaporn Leelaarporn, Maytus Piriyajitakonkij, Thananya Charoenpattarawut, Phairot Autthasan, Rattanaphon Chaisaen, Pathitta Dujada, Thapanun Sudhawiyangkul, Vorapun Senanarong, Theerawit Wilaiprasitporn

The efficiencies of EEG signals from various cognitive tasks, for dementia classification, have yet to be thoroughly investigated.

EEG Specificity

MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification

1 code implementation • 7 Feb 2021 • Phairot Autthasan, Rattanaphon Chaisaen, Thapanun Sudhawiyangkul, Phurin Rangpong, Suktipol Kiatthaveephong, Nat Dilokthanakul, Gun Bhakdisongkhram, Huy Phan, Cuntai Guan, Theerawit Wilaiprasitporn

We integrate deep metric learning into a multi-task autoencoder to learn a compact and discriminative latent representation from EEG and perform classification simultaneously.

Classification EEG +4

Decoding EEG Rhythms During Action Observation, Motor Imagery, and Execution for Standing and Sitting

1 code implementation • 7 Apr 2020 • Rattanaphon Chaisaen, Phairot Autthasan, Nopparada Mingchinda, Pitshaporn Leelaarporn, Narin Kunaseth, Suppakorn Tammajarung, Poramate Manoonpong, Theerawit Wilaiprasitporn

Event-related desynchronization and synchronization (ERD/S) and movement-related cortical potential (MRCP) play an important role in brain-computer interfaces (BCI) for lower limb rehabilitation, particularly in standing and sitting.

Human-Computer Interaction Signal Processing