Enhancement of SSVEPs Classification in BCI-Based Wearable Instrumentation Through Machine Learning Techniques

This work addresses the adoption of Machine Learning classifiers and Convolutional Neural Networks to improve the performance of highly wearable, single-channel instrumentation for Brain-Computer Interfaces. The proposed measurement system is based on the classification of Steady- State Visually Evo...

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Bibliographic Details
Published in:IEEE sensors journal 2022-05, Vol.22 (9), p.9087-9094
Main Authors: Apicella, Andrea, Arpaia, Pasquale, De Benedetto, Egidio, Donato, Nicola, Duraccio, Luigi, Giugliano, Salvatore, Prevete, Roberto
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Augmented reality
BCI
brain-computer interface
Classification
Classification algorithms
Convolutional neural networks
EEG
Electroencephalography
Helmet mounted displays
Human-computer interface
industry 4.0
instrumentation
Instruments
Machine learning
neural networks
Performance enhancement
real-time systems
Sensors
SSVEP
Support vector machines
wearable
Wearable technology
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Summary:This work addresses the adoption of Machine Learning classifiers and Convolutional Neural Networks to improve the performance of highly wearable, single-channel instrumentation for Brain-Computer Interfaces. The proposed measurement system is based on the classification of Steady- State Visually Evoked Potentials (SSVEPs). In particular, Head-Mounted Displays for Augmented Reality are used to generate and display the flickering stimuli for the SSVEPs elicitation. Four experiments were conducted by employing, in turn, a different Head-Mounted Display. For each experiment, two different algorithms were applied and compared with the state-of-the-art-techniques. Furthermore, the impact of different Augmented Reality technologies in the elicitation and classification of SSVEPs was also explored. The experimental metrological characterization demonstrates (i) that the proposed Machine Learning-based processing strategies providea significantenhancement of theSSVEPclassification accuracy with respect to the state of the art, and (ii) that choosing an adequate Head-Mounted Display is crucial to obtain acceptable performance. Finally, it is also shown that the adoption of inter-subjective validation strategies such as the Leave-One-Subject-Out Cross Validation successfully leads to an increase in the inter-individual 1- \sigma reproducibility: this, in turn, anticipates an easier development of ready-to-use systems.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2022.3161743