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Evaluation of Hyperparameter Optimization in Machine and Deep Learning Methods for Decoding Imagined Speech EEG.

Ciaran Cooney¹, Attila Korik¹, Raffaella Folli²

¹Intelligent Systems Research Centre, Ulster University, Londonderry BT48 7JL, UK.

Sensors (Basel, Switzerland)

|August 23, 2020

Summary

This summary is machine-generated.

Deep learning (DL) models, specifically convolutional neural networks (CNNs), significantly outperform traditional machine learning for classifying imagined speech electroencephalography (EEG) signals. Hyperparameter optimization is crucial for maximizing CNN performance in direct-speech brain-computer interface development.

Keywords:

brain–computer interface (BCI)convolutional neural networks (CNN)deep learning electroencephalography (EEG)hyperparameter optimization imagined speech machine learning

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Area of Science:

Neuroscience
Computer Science
Biomedical Engineering

Background:

Direct-speech brain-computer interfaces (DS-BCIs) rely on accurate classification of electroencephalography (EEG) signals from imagined speech.
Deep learning (DL) shows promise, but its superiority over traditional machine learning (ML) for this task and the impact of hyperparameter (HP) optimization are unclear.

Purpose of the Study:

To enhance imagined speech EEG classification using DL methods.
To statistically evaluate the impact of HP optimization on DL classifier performance for DS-BCI applications.

Main Methods:

Trained three distinct convolutional neural networks (CNNs) designed for EEG decoding on an imagined speech dataset (words and vowels).
Employed nested cross-validation for HP optimization and compared CNNs against Support Vector Machine, Random Forest, and Linear Discriminant Analysis.
Investigated intra- and inter-subject HP optimization methods and statistically analyzed HP effects.

Main Results:

CNNs achieved significantly higher accuracies than benchmark ML methods for both word (24.97%) and vowel (30.00%) imagined speech classification (p < 1 × 10⁻⁷).
Both varying HP values and their interactions with CNNs had statistically significant effects on classifier performance.

Conclusions:

DL, particularly CNNs, offers a significant advancement over traditional ML for imagined speech EEG classification.
Rigorous HP optimization is critical for achieving optimal performance in DL-based DS-BCI systems.