Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Source Aware Deep Learning Framework for Hand Kinematic Reconstruction
  using EEG Signal   The ability to reconstruct the kinematic parameters of hand movement using
non-invasive electroencephalography (EEG) is essential for strength and
endurance augmentation using exosuit/exoskeleton. For system development, the
conventional classification based brain computer interface (BCI) controls
external devices by providing discrete control signals to the actuator. A
continuous kinematic reconstruction from EEG signal is better suited for
practical BCI applications. The state-of-the-art multi-variable linear
regression (mLR) method provides a continuous estimate of hand kinematics,
achieving maximum correlation of upto 0.67 between the measured and the
estimated hand trajectory. In this work, three novel source aware deep learning
models are proposed for motion trajectory prediction (MTP). In particular,
multi layer perceptron (MLP), convolutional neural network - long short term
memory (CNN-LSTM), and wavelet packet decomposition (WPD) CNN-LSTM are
presented. Additional novelty of the work includes utilization of brain source
localization (using sLORETA) for the reliable decoding of motor intention
mapping (channel selection) and accurate EEG time segment selection.
Performance of the proposed models are compared with the traditionally utilised
mLR technique on the real grasp and lift (GAL) dataset. Effectiveness of the
proposed framework is established using the Pearson correlation coefficient and
trajectory analysis. A significant improvement in the correlation coefficient
is observed when compared with state-of-the-art mLR model. Our work bridges the
gap between the control and the actuator block, enabling real time BCI
implementation.

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The categories of this paper are: cmp-lg
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The categories of this paper are: bayes-an
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-lat
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The categories of this paper are: cond-mat
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The categories of this paper are: solv-int
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The categories of this paper are: patt-sol
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The categories of this paper are: q-alg
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The categories of this paper are: alg-geom
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-th
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The categories of this paper are: ao-sci
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The categories of this paper are: stat
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The categories of this paper are: chao-dyn
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The categories of this paper are: chem-ph
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The categories of this paper are: q-fin