DAMTRNN: a Delta Attention-Based Multi-task RNN for Intention Recognition

Weitong Chen*, Lin Yue, Bohan Li, Can Wang, Quan Z. Sheng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

7 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationAdvanced Data Mining and Applications
Subtitle of host publication15th International Conference, ADMA 2019, Proceedings
EditorsJianxin Li, Sen Wang, Shaowen Qin, Xue Li, Shuliang Wang
Place of PublicationCham, Switzerland
PublisherSpringer, Springer Nature
Number of pages16
ISBN (Electronic)9783030352318
ISBN (Print)9783030352301
Publication statusPublished - 2019
Event15th International Conference on Advanced Data Mining and Applications, ADMA 2019 - Dalian, China
Duration: 21 Nov 201923 Nov 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11888 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th International Conference on Advanced Data Mining and Applications, ADMA 2019


  • Attention network
  • Human intention recognition
  • Multi-task learning


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