On the Classification of SSVEP-Based Dry-EEG Signals via Convolutional Neural Networks

Abstract

Electroencephalography (EEG) is a common signal acquisition approach employed for Brain-Computer Interface (BCI) research. Nevertheless, the majority of EEG acquisition devices rely on the cumbersome application of conductive gel (so-called wet-EEG) to ensure a high quality signal is obtained. However, this process is unpleasant for the experimental participants and thus limits the practical application of BCI. In this work, we explore the use of a commercially available dry-EEG headset to obtain visual cortical ensemble signals. Whilst improving the usability of EEG within the BCI context, dry-EEG suffers from inherently reduced signal quality due to the lack of conduit gel, making the classification of such signals significantly more challenging. In this paper, we propose a novel Convolutional Neural Network (CNN) approach for the classification of raw dry-EEG signals without any data pre-processing. To illustrate the effectiveness of our approach, we utilise the Steady State Visual Evoked Potential (SSVEP) paradigm as our use case. SSVEP can be utilised to allow people with severe physical disabilities such as Complete Locked-In Syndrome or Amyotrophic Lateral Sclerosis to be aided via BCI applications, as it requires only the subject to fixate upon the sensory stimuli of interest. Here we utilise SSVEP flicker frequencies between 10 to 30 Hz, which we record as subject cortical waveforms via the dry-EEG headset. Our proposed end-to-end CNN allows us to automatically and accurately classify SSVEP stimulation directly from the dry-EEG waveforms. Our CNN architecture utilises a common SSVEP Convolutional Unit (SCU), comprising of a 1D convolutional layer, batch normalization and max pooling. Furthermore, we compare several deep learning neural network variants with our primary CNN architecture, in addition to traditional machine learning classification approaches. Experimental evaluation shows our CNN architecture to be significantly better than competing approaches, achieving a classification accuracy of 96% whilst demonstrating superior cross-subject performance and even being able to generalise well to unseen subjects whose data is entirely absent from the training process.

Deep learning-based electroencephalography analysis: a systematic review

“…Since the observed improvement will likely be higher when comparing to simple baselines than to state-of-the-art results, the values that we report might be biased positively. For instance, only two studies used Riemannian geometry-based processing pipelines as baseline models [11,87], although these methods have set a new state-of-the-art in multiple EEG classification tasks [105].…”

“…Almogbel et al [7] used raw EEG data to classify cognitive workload in vehicle drivers, and their best model achieved a classification accuracy approximately 4% better than their benchmarks which employed preprocessing on the EEG data. Similarly, Aznan et al [11] outperformed the baselines by a 4% margin on SSVEP decoding using no preprocessing. Thus, the answer to whether it is necessary to preprocess EEG data when using DNNs remains elusive.…”

Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation

“…To decode the dry-EEG signals efficiently in order to ensure effective teleoperation of the robot, we use our deep CNN architecture of [17]) (see reference for more details) for signal to object/motion label classification.…”

“…The cortical brain signals from each subject are collected for 40 experimental trials per SSVEP class to form the offline a priori training sets or training the CNN model per subject (offline calibration). We train a SSVEP Convolutional Unit (SCU) CNN architecture [17], comprising of a 1D convolutional layer, batch normalization and max pooling (as detailed in Figure 4) by using the offline priori experimental datasets. We first bandpass filter the incoming sigmals between 9 to 100 Hz in order to reduce undesired high or low frequencies that are not of interest in this work.…”

“…To perform the dry-EEG signal decoding we use CNN architecture, detailed here [17], to differentiate between EEG signals by extracting unique features across multiple layers of convolutional transformation optimised over a set of training data [18]. This model is used to classify real-time dry-EEG signals before sending the decoded command to navigate the robot towards the scene object the subject has selected [19].…”