PMID- 37603133
OWN - NLM
STAT- MEDLINE
DCOM- 20231211
LR  - 20231211
IS  - 2662-4737 (Electronic)
IS  - 2662-4729 (Linking)
VI  - 46
IP  - 4
DP  - 2023 Dec
TI  - Development of hybrid feature learner model integrating FDOSM for golden subject 
      identification in motor imagery.
PG  - 1519-1534
LID - 10.1007/s13246-023-01316-6 [doi]
AB  - Brain-computer interfaces (BCIs) based on motor imagery (MI) face challenges due 
      to the complex nature of brain activity, nonstationary and high-dimensional 
      properties, and individual variations in motor behaviour. The identification of a 
      consistent "golden subject" in MI-based BCIs remains an open challenge, 
      complicated by multiple evaluation metrics and conflicting trade-offs, presenting 
      complex Multi-Criteria Decision Making (MCDM) problems. This study proposes a 
      hybrid brain signal decoding model called Hybrid Adaboost Feature Learner (HAFL), 
      which combines feature extraction and classification using VGG-19, STFT, and 
      Adaboost classifier. The model is validated using a pre-recorded MI-EEG dataset 
      from the BCI competition at Graz University. The fuzzy decision-making framework 
      is integrated with HAFL to allocate a golden subject for MI-BCI applications 
      through the Golden Subject Decision Matrix (GSDM) and the Fuzzy Decision by 
      Opinion Score Method (FDOSM). The effectiveness of the HAFL model in addressing 
      inter-subject variability in EEG-based MI-BCI is evaluated using an MI-EEG 
      dataset involving nine subjects. Comparing subject performance fairly is 
      challenging due to complexity variations, but the FDOSM method provides valuable 
      insights. Through FDOSM-based External Group Aggregation (EGA), subject S5 
      achieves the highest score of 2.900, identified as the most promising golden 
      subject for subject-to-subject transfer learning. The proposed methodology is 
      compared against other benchmark studies from various key perspectives and 
      exhibits significant novelty in several aspects. The findings contribute to the 
      development of more robust and effective BCI systems, paving the way for 
      advancements in subject-to-subject transfer learning for BCI-MI applications.
CI  - (c) 2023. Australasian College of Physical Scientists and Engineers in Medicine.
FAU - Al-Qaysi, Z T
AU  - Al-Qaysi ZT
AD  - Department of Computer Science, Computer Science and Mathematics College, Tikrit 
      University, Tikrit, Iraq.
FAU - Albahri, A S
AU  - Albahri AS
AUID- ORCID: 0000-0003-3335-457X
AD  - Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq. 
      ahmed.bahri1978@gmail.com.
FAU - Ahmed, M A
AU  - Ahmed MA
AD  - Department of Computer Science, Computer Science and Mathematics College, Tikrit 
      University, Tikrit, Iraq.
FAU - Mohammed, Saleh Mahdi
AU  - Mohammed SM
AD  - Department of Computer Technology Engineering, College of Information Technology, 
      Imam Ja'afar Al-Sadiq University, Baghdad, Iraq.
LA  - eng
PT  - Journal Article
DEP - 20230821
PL  - Switzerland
TA  - Phys Eng Sci Med
JT  - Physical and engineering sciences in medicine
JID - 101760671
SB  - IM
MH  - Humans
MH  - *Electroencephalography/methods
MH  - Imagination
MH  - Imagery, Psychotherapy
MH  - *Brain-Computer Interfaces
MH  - Learning
OTO - NOTNLM
OT  - Brain-computer interface
OT  - FDOSM
OT  - Golden subject
OT  - Motor imagery
OT  - Short-time fourier transform
OT  - Transfer learning
OT  - VGG-19
EDAT- 2023/08/21 12:43
MHDA- 2023/12/11 12:43
CRDT- 2023/08/21 11:07
PHST- 2022/08/13 00:00 [received]
PHST- 2023/08/01 00:00 [accepted]
PHST- 2023/12/11 12:43 [medline]
PHST- 2023/08/21 12:43 [pubmed]
PHST- 2023/08/21 11:07 [entrez]
AID - 10.1007/s13246-023-01316-6 [pii]
AID - 10.1007/s13246-023-01316-6 [doi]
PST - ppublish
SO  - Phys Eng Sci Med. 2023 Dec;46(4):1519-1534. doi: 10.1007/s13246-023-01316-6. Epub 
      2023 Aug 21.