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      A novel hybrid deep learning scheme for four-class motor imagery classification

      Author(s): , , ,
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      Journal: Journal of Neural Engineering
      Publisher: IOP Publishing

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          Abstract

          <p class="first" id="d6123097e71">Learning the structures and unknown correlations of a motor imagery electroencephalogram (MI-EEG) signal is important for its classification. It is also a major challenge to obtain good classification accuracy from the increased number of classes and increased variability from different people. In this study, a four-class MI task is investigated. </p>

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          Deep learning.

          Yann LeCun, Yoshua Bengio, Geoffrey E Hinton (2015)
          Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
          Article 0 comments Cited 10057 times – based on 0 reviews     Review now
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            A Coefficient of Agreement for Nominal Scales

            Mitchell Cohen (1960)
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              Deep learning in neural networks: An overview

              Jürgen Schmidhuber (2015)
              In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
              Article 0 comments Cited 1927 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Journal
                Title: Journal of Neural Engineering
                Abbreviated Title: J. Neural Eng.
                Publisher: IOP Publishing
                ISSN (Electronic): 1741-2552
                Publication date Created: December 01 2019
                Publication date (Electronic): October 16 2019
                Volume: 16
                Issue: 6
                Page: 066004
                Article
                DOI: 10.1088/1741-2552/ab3471
                PubMed ID: 31341093
                SO-VID: 0c45a7b6-3ab8-434c-a148-64dbf17c65ee
                Copyright © © 2019
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                http://iopscience.iop.org/info/page/text-and-data-mining

                http://iopscience.iop.org/page/copyright

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