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

Aznan, Nik Khadijah Nik; Connolly, Jason D.; Moubayed, Noura Al; Breckon, Toby P.

doi:10.1109/icra.2019.8794060

Cited by 23 publications

(24 citation statements)

References 23 publications

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“…In classifier performance evaluation, the synthetic data size was 3828, and it was added to the training dataset during training. [81]. The NAO dataset has two portions-offline and online-collected from tasks the same as in the Video-Stimuli dataset using a dry EEG device with 20 channels.…”

Section: Noise Additionmentioning

confidence: 99%

Data augmentation for deep-learning-based electroencephalography

Lashgari

Liang

Maoz

2020

Journal of Neuroscience Methods

277

140

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Highlights • Data augmentation (DA) is increasingly used with deep learning (DL) on EEG • It enhances decoding accuracy left unexplained by 29% on average on the datasets we review • We analyze which specific DA techniques appear to work best for which EEG tasks • We tested various DA techniques on an open motor-imagery task and compared the accuracy gains to demonstrate the usefulness of DA for DL-based EEG analysis • We propose guidelines for reporting parameters for different DA techniques Abstract-Background Data augmentation (DA) has recently been demonstrated to achieve considerable performance gains for deep learning (DL)-increased accuracy and stability and reduced overfitting. Some electroencephalography (EEG) tasks suffer from low samples-to-features ratio, severely reducing DL effectiveness. DA with DL thus holds transformative promise for EEG processing, possibly like DL revolutionized computer vision, etc.-New method We review trends and approaches to DA for DL in EEG to address: Which DA approaches exist and are common for which EEG tasks? What input features are used? And, what kind of accuracy gain can be expected?-Results DA for DL on EEG begun 5 years ago and is steadily used more. We grouped DA techniques (noise addition, generative adversarial networks, sliding windows, sampling, Fourier transform, recombination of segmentation, and others) and EEG tasks (into seizure detection, sleep stages, motor imagery, mental workload, emotion recognition, motor tasks, and visual tasks). DA efficacy across techniques varied considerably. Noise addition and sliding windows provided the highest accuracy boost; mental workload most benefitted from DA. Sliding window, noise addition, and sampling methods most common for seizure detection, mental workload, and sleep stages, respectively.-Comparing with existing methods Percent of decoding accuracy explained by DA beyond unaugmented accuracy varied between 8% for recombination of segmentation and 36% for noise addition and from 14% for motor imagery to 56% for mental workload-29% on average.-Conclusions DA increasingly used and considerably improved DL decoding accuracy on EEG. Additional publications-if adhering to our reporting guidelines-will facilitate more detailed analysis.

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Section: Noise Additionmentioning

confidence: 99%

Data augmentation for deep-learning-based electroencephalography

Lashgari

Liang

Maoz

2020

Journal of Neuroscience Methods

277

140

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“…This section will detail the setup of our experimental evaluation, including introducing the empirical datasets used, detailing how we generate new data samples from our generative models and our procedure for evaluating the quality of the generated data. We make use of two empirical SSVEP dry-EEG datasets which we collected and fully detailed in our previous work [18]. The collection procedure for this dataset is detailed in Figure 4.…”

Section: Methodsmentioning

confidence: 99%

“…The offline portion of the data contains 50 unique samples for each of the three classes taken from three subjects (S01, S02, S03) performing the SSVEP task by looking at objects detected in a prerecorded video sequence from a humanoid robot. The online portion of the data contains 30 samples per class taken from the same three subjects when navigating the robot in real time [18].…”

Section: A Empirical Ssvep Dry-eeg Datasetsmentioning

confidence: 99%

“…The frequency of the flashing is present in the EEG signals recorded from the subjects and can be extracted via a variety of competing signal processing techniques [16]. SSVEP has many important applications in BCI, for example it can be utilised to allow people with severe physical disabilities to control or communicate with external devices just by having them fixate on a flickering stimuli [17], controlling an exoskeleton [4] or navigating a humanoid robot [18]. To the best of our knowledge, this is the first work in the literature to explore the use of neural-based generative models to create dry-EEG data containing SSVEP information.…”

Section: Introductionmentioning

confidence: 99%

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Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification

Aznan

Atapour-Abarghouei

Bonner

et al. 2019

2019 International Joint Conference on Neural Networks (IJCNN)

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Despite significant recent progress in the area of Brain-Computer Interface (BCI), there are numerous shortcomings associated with collecting Electroencephalography (EEG) signals in real-world environments. These include, but are not limited to, subject and session data variance, long and arduous calibration processes and predictive generalisation issues across different subjects or sessions. This implies that many downstream applications, including Steady State Visual Evoked Potential (SSVEP) based classification systems, can suffer from a shortage of reliable data. Generating meaningful and realistic synthetic data can therefore be of significant value in circumventing this problem. We explore the use of modern neural-based generative models trained on a limited quantity of EEG data collected from different subjects to generate supplementary synthetic EEG signal vectors, subsequently utilised to train an SSVEP classifier. Extensive experimental analysis demonstrates the efficacy of our generated data, leading to improvements across a variety of evaluations, with the crucial task of cross-subject generalisation improving by over 35% with the use of such synthetic data.

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“…Experiments show that presentation-associated patterns of EEG could be seen clearly in generated data and they obtained significant improvement based on the EEGNet model after DA in the RSVP task (Lawhern et al, 2018). A similar method was also performed in Aznan et al (2019). Aznan et al (2019) applied WGAN to generate synthetic EEG data that optimizes the efficiency of interaction in the SSVEP task.…”

Section: Data Augmentation Strategy For Eeg Classificationmentioning

confidence: 99%

Data Augmentation for Deep Neural Networks Model in EEG Classification Task: A Review

He¹,

Liu²,

Zhu

et al. 2021

Front. Hum. Neurosci.

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Classification of electroencephalogram (EEG) is a key approach to measure the rhythmic oscillations of neural activity, which is one of the core technologies of brain-computer interface systems (BCIs). However, extraction of the features from non-linear and non-stationary EEG signals is still a challenging task in current algorithms. With the development of artificial intelligence, various advanced algorithms have been proposed for signal classification in recent years. Among them, deep neural networks (DNNs) have become the most attractive type of method due to their end-to-end structure and powerful ability of automatic feature extraction. However, it is difficult to collect large-scale datasets in practical applications of BCIs, which may lead to overfitting or weak generalizability of the classifier. To address these issues, a promising technique has been proposed to improve the performance of the decoding model based on data augmentation (DA). In this article, we investigate recent studies and development of various DA strategies for EEG classification based on DNNs. The review consists of three parts: what kind of paradigms of EEG-based on BCIs are used, what types of DA methods are adopted to improve the DNN models, and what kind of accuracy can be obtained. Our survey summarizes the current practices and performance outcomes that aim to promote or guide the deployment of DA to EEG classification in future research and development.

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

Cited by 23 publications

References 23 publications

Data augmentation for deep-learning-based electroencephalography

Data augmentation for deep-learning-based electroencephalography

Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification

Data Augmentation for Deep Neural Networks Model in EEG Classification Task: A Review

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