@inproceedings{0f7dcaff4ae245d1ad8cd3c58d3bc7aa,
title = "DAMTRNN: a Delta Attention-Based Multi-task RNN for Intention Recognition",
abstract = "Recognizing human intentions from electroencephalographic (EEG) signals is attracting extraordinary attention from the artificial intelligence community because of its promise in providing non-muscular forms of communication and control to those with disabilities. So far, studies have explored correlations between specific segments of an EEG signal and an associated intention. However, there are still challenges to be overcome on the road ahead. Among these, vector representations suffer from the enormous amounts of noise that characterize EEG signals. Identifying the correlations between signals from adjacent sensors on a headset is still difficult. Further, research not yet reached the point where learning models can accept decomposed EEG signals to capture the unique biological significance of the six established frequency bands. In pursuit of a more effective intention recognition method, we developed DAMTRNN, a delta attention-based multi-task recurrent neural network, for human intention recognition. The framework accepts divided EEG signals as inputs, and each frequency range is modeled separately but concurrently with a series of LSTMs. A delta attention network fuses the spatial and temporal interactions across different tasks into high-impact features, which captures correlations over longer time spans and further improves recognition accuracy. Comparative evaluations between DAMTRNN and 14 state-of-the-art methods and baselines show DAMTRNN with a record-setting performance of 98.87% accuracy.",
keywords = "Attention network, Human intention recognition, Multi-task learning",
author = "Weitong Chen and Lin Yue and Bohan Li and Can Wang and Sheng, {Quan Z.}",
year = "2019",
doi = "10.1007/978-3-030-35231-8_27",
language = "English",
isbn = "9783030352301",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer, Springer Nature",
pages = "373--388",
editor = "Jianxin Li and Sen Wang and Shaowen Qin and Xue Li and Shuliang Wang",
booktitle = "Advanced Data Mining and Applications",
address = "United States",
note = "15th International Conference on Advanced Data Mining and Applications, ADMA 2019 ; Conference date: 21-11-2019 Through 23-11-2019",
}