TY - GEN
T1 - Accelerating Brain Research using Explainable Artificial Intelligence
AU - Chou, Jing Lun
AU - Huang, Ya Lin
AU - Hsieh, Chia Ying
AU - Huang, Jian Xue
AU - Wei, Chun Shu
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In this demo, we present ExBrainable, an open-source application dedicated to modeling, evaluating and visualizing explainable CNN-based models on EEG data for brain/neuroscience research. We have implemented the functions including EEG data loading, model training, evaluation and parameter visualization. The application is also built with a model base including representative convolutional neural network architectures for users to implement without any programming. With its easy-to-use graphic user interface (GUI), it is completely available for investigators of different disciplines with limited resource and limited programming skill. Starting with preprocessed EEG data, users can quickly train the desired model, evaluate the performance, and finally visualize features learned by the model with no pain.
AB - In this demo, we present ExBrainable, an open-source application dedicated to modeling, evaluating and visualizing explainable CNN-based models on EEG data for brain/neuroscience research. We have implemented the functions including EEG data loading, model training, evaluation and parameter visualization. The application is also built with a model base including representative convolutional neural network architectures for users to implement without any programming. With its easy-to-use graphic user interface (GUI), it is completely available for investigators of different disciplines with limited resource and limited programming skill. Starting with preprocessed EEG data, users can quickly train the desired model, evaluate the performance, and finally visualize features learned by the model with no pain.
KW - Brain-computer interface (BCI)
KW - convolutional neural network (CNN)
KW - electroencephalography (EEG)
KW - feature visualization
UR - http://www.scopus.com/inward/record.url?scp=85138040598&partnerID=8YFLogxK
U2 - 10.1109/ICMEW56448.2022.9859322
DO - 10.1109/ICMEW56448.2022.9859322
M3 - Conference contribution
AN - SCOPUS:85138040598
T3 - ICMEW 2022 - IEEE International Conference on Multimedia and Expo Workshops 2022, Proceedings
BT - ICMEW 2022 - IEEE International Conference on Multimedia and Expo Workshops 2022, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2022
Y2 - 18 July 2022 through 22 July 2022
ER -