Interpretable deep neural networks for single-trial EEG classification

Autor: Wojciech Samek, Klaus-Robert Müller, Irene Sturm, Sebastian Lapuschkin
Přispěvatelé: Publica
Rok vydání: 2016
Předmět:
FOS: Computer and information sciences
Computer science
media_common.quotation_subject
Machine Learning (stat.ML)
02 engineering and technology
Cognitive neuroscience
Machine learning
computer.software_genre
03 medical and health sciences
0302 clinical medicine
Statistics - Machine Learning
Perception
0202 electrical engineering
electronic engineering
information engineering
Animals
Humans
Relevance (information retrieval)
Neural and Evolutionary Computing (cs.NE)
Brain–computer interface
media_common
Interpretability
Neurons
Brain Mapping
Artificial neural network
business.industry
General Neuroscience
Computer Science - Neural and Evolutionary Computing
Brain
Electroencephalography
Neurophysiology
Brain Waves
Deep neural networks
020201 artificial intelligence & image processing
Artificial intelligence
Nerve Net
business
computer
030217 neurology & neurosurgery
Zdroj: Journal of Neuroscience Methods. 274:141-145
ISSN: 0165-0270
Popis: Background: 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. New Method: 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. Results: 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. Comparison with Existing Method(s): We compare the classification performance of DNNs to that of linear CSP-LDA on two data sets related to motor-imaginery BCI. Conclusion: 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.
Comment: 5 pages, 1 figure
Databáze: OpenAIRE
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