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- W4225926968 abstract "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-<inline-formula> <tex-math notation=LaTeX>$sigma$ </tex-math></inline-formula> reproducibility: this, in turn, anticipates an easier development of ready-to-use systems." @default.
- W4225926968 created "2022-05-05" @default.
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- W4225926968 date "2022-05-01" @default.
- W4225926968 modified "2023-10-09" @default.
- W4225926968 title "Enhancement of SSVEPs Classification in BCI-Based Wearable Instrumentation Through Machine Learning Techniques" @default.
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- W4225926968 doi "https://doi.org/10.1109/jsen.2022.3161743" @default.
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