title: Interpretable deep neural networks for single-trial EEG classification.
abstract: In cognitive neuroscience the potential of deep neural networks (DNNs) for solving complex classification tasks is yet to be fully exploited. The most limiting factor is that DNNs as notorious 'black boxes' do not provide insight into neurophysiological phenomena underlying a decision. Layer-wise relevance propagation (LRP) has been introduced as a novel method to explain individual network decisions.
  
  
  
  We propose the application of DNNs with LRP for the first time for EEG data analysis. Through LRP the single-trial DNN decisions are transformed into heatmaps indicating each data point's relevance for the outcome of the decision.
  
  
  
  DNN achieves classification accuracies comparable to those of CSP-LDA. In subjects with low performance subject-to-subject transfer of trained DNNs can improve the results. The single-trial LRP heatmaps reveal neurophysiologically plausible patterns, resembling CSP-derived scalp maps. Critically, while CSP patterns represent class-wise aggregated information, LRP heatmaps pinpoint neural patterns to single time points in single trials.
  
  
  
  We compare the classification performance of DNNs to that of linear CSP-LDA on two data sets related to motor-imaginary BCI.
  
  
  
  We have demonstrated that DNN is a powerful non-linear tool for EEG analysis. With LRP a new quality of high-resolution assessment of neural activity can be reached. LRP is a potential remedy for the lack of interpretability of DNNs that has limited their utility in neuroscientific applications. The extreme specificity of the LRP-derived heatmaps opens up new avenues for investigating neural activity underlying complex perception or decision-related processes.
authors.affiliation: Machine Learning Group, Berlin Institute of Technology, Marchstr. 23, 10587 Berlin, Germany. Electronic address: irene.sturm@tu-berlin.de.
authors.forename: Irene
authors.initials: I
authors.lastname: Sturm
authors.affiliation: Machine Learning Group, Fraunhofer Heinrich Hertz Institute, Einsteinufer 37, 10587 Berlin, Germany. Electronic address: sebastian.lapuschkin@hhi.fraunhofer.de.
authors.forename: Sebastian
authors.initials: S
authors.lastname: Lapuschkin
authors.affiliation: Machine Learning Group, Fraunhofer Heinrich Hertz Institute, Einsteinufer 37, 10587 Berlin, Germany. Electronic address: wojciech.samek@hhi.fraunhofer.de.
authors.forename: Wojciech
authors.initials: W
authors.lastname: Samek
authors.affiliation: Machine Learning Group, Berlin Institute of Technology, Marchstr. 23, 10587 Berlin, Germany; Department of Brain and Cognitive Engineering, Korea University, Anam-dong, Seongbuk-gu, Seoul 136-713, Republic of Korea. Electronic address: klaus-robert.mueller@tu-berlin.de.
authors.forename: Klaus-Robert
authors.initials: KR
authors.lastname: Müller
mesh_terms: D000818:Animals; D001921:Brain / Q000166:cytology* / Q000502:physiology*; D001931:Brain Mapping; D058256:Brain Waves / Q000502:physiology*; D004569:Electroencephalography / Q000145:classification* / Q000379:methods; D006801:Humans; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*
keywords: Brain–computer interfacing; Interpretability; Neural networks