Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded
  Motor-Imagery Brain-Machine Interfaces   In recent years, deep learning (DL) has contributed significantly to the
improvement of motor-imagery brain-machine interfaces (MI-BMIs) based on
electroencephalography(EEG). While achieving high classification accuracy, DL
models have also grown in size, requiring a vast amount of memory and
computational resources. This poses a major challenge to an embedded BMI
solution that guarantees user privacy, reduced latency, and low power
consumption by processing the data locally. In this paper, we propose
EEG-TCNet, a novel temporal convolutional network (TCN) that achieves
outstanding accuracy while requiring few trainable parameters. Its low memory
footprint and low computational complexity for inference make it suitable for
embedded classification on resource-limited devices at the edge. Experimental
results on the BCI Competition IV-2a dataset show that EEG-TCNet achieves
77.35% classification accuracy in 4-class MI. By finding the optimal network
hyperparameters per subject, we further improve the accuracy to 83.84%.
Finally, we demonstrate the versatility of EEG-TCNet on the Mother of All BCI
Benchmarks (MOABB), a large scale test benchmark containing 12 different EEG
datasets with MI experiments. The results indicate that EEG-TCNet successfully
generalizes beyond one single dataset, outperforming the current
state-of-the-art (SoA) on MOABB by a meta-effect of 0.25.

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The categories of this paper are: astro-ph
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The categories of this paper are: math
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The categories of this paper are: q-alg
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The categories of this paper are: bayes-an
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The categories of this paper are: mtrl-th
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The categories of this paper are: acc-phys
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The categories of this paper are: funct-an
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The categories of this paper are: comp-gas
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The categories of this paper are: adap-org
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The categories of this paper are: solv-int
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The categories of this paper are: hep-lat
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The categories of this paper are: plasm-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-th
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The categories of this paper are: math-ph
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The categories of this paper are: hep-ex