EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain-Machine Interfaces

Ingolfsson, Thorir Mar; Hersche, Michael; Wang, Xiaying; Kobayashi, Nobuaki; Cavigelli, Lukas; Benini, Luca

doi:10.48550/arxiv.2006.00622

Cited by 4 publications

(6 citation statements)

References 28 publications

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“…This suggests that the MI classification needs more abilities to extract long-time-wise information. This observation is also supported by previous works (Riyad, Khalil, and Adib 2020;Roots, Muhammad, and Muhammad 2020;Ingolfsson et al 2020), which suggests that time-wise signal aggregation is of vital importance for MI classification.…”

Section: D2 Visualization Of Searched Structuressupporting

confidence: 88%

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Cross Task Neural Architecture Search for EEG Signal Classifications

Duan¹,

Wang²,

Li³

et al. 2022

Preprint

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Electroencephalograms (EEGs) are brain dynamics measured outside of the brain, which have been widely utilized in non-invasive brain-computer interface applications. Recently, various neural network approaches have been proposed to improve the accuracy of EEG signal recognition. However, these approaches severely rely on manually designed network structures for different tasks which normally are not sharing the same empirical design cross-task-wise. In this paper, we propose a cross-task neural architecture search (CTNAS-EEG) framework for EEG signal recognition, which can automatically design the network structure across tasks and improve the recognition accuracy of EEG signals. Specifically, a compatible search space for cross-task searching and an efficient constrained searching method is proposed to overcome challenges brought by EEG signals. By unifying structure search on different EEG tasks, this work is the first to explore and analyze the searched structure difference in crosstask-wise. Moreover, by introducing architecture search, this work is the first to analyze model performance by customizing model structure for each human subject. Detailed experimental results suggest that the proposed CTNAS-EEG could reach state-of-the-art performance on different EEG tasks, such as Motor Imagery (MI) and Emotion recognition. Extensive experiments and detailed analysis are provided as a good reference for follow-up researchers. We will release the source code 1 to contribute to the open source community.

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Section: D2 Visualization Of Searched Structuressupporting

confidence: 88%

“…TPCT (Li, Han, and Duan 2019) combines advantages from both pre-processed feature-based input and raw signals by introducing large-scale CNN. TCNet (Ingolfsson et al 2020) introduces temporal convolution and reaches a good balance between network scale and accuracy.…”

Section: Motor Imaginary (Mi)mentioning

confidence: 99%

Cross Task Neural Architecture Search for EEG Signal Classifications

Duan¹,

Wang²,

Li³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…More recently, other works also suggest model structures specifically optimized for efficient inference. EEG-TCNet [51] proposes a temporal convolutional network (TCN) that achieves impressive accuracy while requiring few trainable parameters. HadaNet [52] proposes a Hadamard variant of the ShuffleNet [48], which more efficiently mixes channel information.…”

Section: Compact Model Designmentioning

confidence: 99%

Enabling Design Methodologies and Future Trends for Edge AI: Specialization and Codesign

et al. 2021

Self Cite

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Artificial intelligence (AI) technologies have dramatically advanced in recent years, resulting in revolutionary changes in people's lives. Empowered by edge computing, AI workloads are migrating from centralized cloud architectures to distributed edge systems, introducing a new paradigm called edge AI. While edge AI has the promise of bringing significant increases in autonomy and intelligence into everyday lives through common edge devices, it also raises new challenges, especially for the development of its algorithms and the deployment of its services, which call for novel design methodologies catered to these unique challenges. In this paper, we provide a comprehensive survey of the latest enabling design methodologies that span the entire edge AI development stack. We suggest that the key methodologies for effective edge AI development are single-layer specialization and cross-layer co-design. We discuss representative methodologies in each category in detail, including on-device training methods, specialized software design, dedicated hardware design, benchmarking and design automation, software/hardware co-design, software/compiler co-design, and compiler/hardware co-design. Moreover, we attempt to reveal hidden cross-layer design opportunities that can further boost the solution quality of future edge AI and provide insights into future directions and emerging areas that require increased research focus.

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“…Gemein et al (2020) applied the EEG-optimized TCN (BD-TCN) in the classification of pathological versus nonpathological EEG, and the results showed an excellent decoding performance. In view of its excellent performance in long sequences, Ingolfsson et al (2020) introduced TCN on the basis of EEGNet and proposed the EEG-TCNet to classify the MI-EEG signals. EEG-TCNet inherited the advantages of EEGNet and achieved excellent classification accuracy with fewer parameters.…”

Section: Introductionmentioning

confidence: 99%

EEG-CDILNet: a lightweight and accurate CNN network using circular dilated convolution for motor imagery classification

Liang

Liu

et al. 2023

J. Neural Eng.

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Objective. The combination of the motor imagery (MI) electroencephalography signals and deep learning-based methods is an effective way to improve MI classification accuracy. However, deep learning-based methods often need too many trainable parameters. As a result, the trade-off between the network decoding performance and computational cost has always been an important challenge in the MI classification research. Approach. In the present study, we proposed a new end-to-end convolutional neural network (CNN) model called the electroencephalography-circular dilated convolution network (EEG-CDILNet), which takes into account both the lightweight model and the classification accuracy. Specifically, the depth-separable convolution was used to reduce the number of network parameters and extract the temporal and spatial features from the EEG signals. Circular dilated convolution (CDIL) was used to extract the time-varying deep features that were generated in the previous stage. Finally, we combined the features extracted from the two stages and used the global average pooling to further reduce the number of parameters, in order to achieve an accurate MI classification. The performance of the proposed model was verified using three publicly available datasets. Results. The proposed model achieved an average classification accuracy of 79.63% and 94.53% for the BCIIV2a and HGD four-classification task, respectively, and 87.82% for the BCIIV2b two-classification task. In particular, by comparing the number of parameters, computation and classification accuracy with other lightweight models, it was confirmed that the proposed model achieved a better balance between the decoding performance and computational cost. Furthermore, the structural feasibility of the proposed model was confirmed by ablation experiments and feature visualization. Significance. The results indicated that the proposed CNN model presented high classification accuracy with less computing resources, and can be applied in the MI classification research.

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EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain-Machine Interfaces

Cited by 4 publications

References 28 publications

Cross Task Neural Architecture Search for EEG Signal Classifications

Cross Task Neural Architecture Search for EEG Signal Classifications

Enabling Design Methodologies and Future Trends for Edge AI: Specialization and Codesign

EEG-CDILNet: a lightweight and accurate CNN network using circular dilated convolution for motor imagery classification

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