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. 2020 Feb 25:14:155.
doi: 10.3389/fnins.2020.00155. eCollection 2020.

Enhanced Multiple Instance Representation Using Time-Frequency Atoms in Motor Imagery Classification

Diego Collazos-Huertas  1 , Julian Caicedo-Acosta  1 , German A Castaño-Duque  2 , Carlos D Acosta-Medina  1
Affiliations

Enhanced Multiple Instance Representation Using Time-Frequency Atoms in Motor Imagery Classification

Diego Collazos-Huertas et al. Front Neurosci. .

Abstract

Selection of the time-window mainly affects the effectiveness of piecewise feature extraction procedures. We present an enhanced bag-of-patterns representation that allows capturing the higher-level structures of brain dynamics within a wide window range. So, we introduce augmented instance representations with extended window lengths for the short-time Common Spatial Pattern algorithm. Based on multiple-instance learning, the relevant bag-of-patterns are selected by a sparse regression to feed a bag classifier. The proposed higher-level structure representation promotes two contributions: (i) accuracy improvement of bi-conditional tasks, (ii) A better understanding of dynamic brain behavior through the learned sparse regression fits. Using a support vector machine classifier, the achieved performance on a public motor imagery dataset (left-hand and right-hand tasks) shows that the proposed framework performs very competitive results, providing robustness to the time variation of electroencephalography recordings and favoring the class separability.

Keywords: CSP; LASSO regularization; dynamic brain behavior; motor imagery; multiple-instance learning.

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Figures

Figure 1
Figure 1
Scheme of bag-of-patterns representation proposed for classification of bi-class motor imagery tasks. Within the MIL framework using t-f atoms, the suggested improvement is remarked by a dashed box.
Figure 2
Figure 2
Estimation of J using the optimal time window τ. (Left) the subject A08 (achieving the best accuracy), (Center) the patient A02 (worst accuracy), and (Right) the group analysis performed at the admitted value τ = 2 s for validating the tested MI Dataset 2a. Spectral relevance is colored in gray bars.
Figure 3
Figure 3
Accuracy performed at different window length combinations of atom-based instances. The last row beneath the dotted line displays the subject performance with a lower accuracy (A06T, A04T, and A02T) after using the optimization of the atom-based MIL representation stage.
Figure 4
Figure 4
Temporal dynamics from the absolute LASSO weights performed within the motor imagery period. Each time series is a cross-validated fold. The last row displays the subjects with lower accuracy after the optimization of the atom-based MIL representation stage.
Figure 5
Figure 5
Pairwise similarity between subjects across the trial set assessed when omitting the optimization of t-f atoms (Left) and using the Multiple-Instance Logistic Regression (Right).
See this image and copyright information in PMC

References

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