eyeSay: Eye Electrooculography Decoding with Deep Learning

Zou, Jiadao; Zhang, Qingxue

doi:10.1109/icce50685.2021.9427710

Cited by 10 publications

(6 citation statements)

References 8 publications

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“…While we applied our approach within the context of automated MDD diagnosis, our approach could also easily be applied to other EEG applications. Moreover, given the variety of waveforms found in EEG sleep data, our approach has the potential to be applied within the context of other electrophysiology modalities like magnetoencephalography [41], electromyography [42], electrooculography [43], or multimodal sleep staging [25], [44]. This form of cross-modality pretraining could greatly accelerate model development across a variety of electrophysiology modalities.…”

Section: Resultsmentioning

confidence: 99%

Improving Multichannel Raw Electroencephalography-based Diagnosis of Major Depressive Disorder via Transfer Learning with Single Channel Sleep Stage Data*

Ellis

Miller

Calhoun

2023

Preprint

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Deep learning methods are increasingly being applied to raw electroencephalography (EEG) data. Relative to traditional machine learning methods, deep learning methods can increase model performance through automated feature extraction. Nevertheless, they are also less explainable. Existing raw EEG explainability methods identify relative feature importance but do not identify how features relate to model predictions. In this study, we combine well-characterized first layer filters with a novel post hoc statistical analysis of the filter activations that linearly relates properties of model activations and predictions. We implement our approach within the context of automated sleep stage classification, finding that the model uncovers positive relationships between waveforms resembling sleep spindles and NREM2, between high frequency filters and Awake samples, and between delta activity and NREM3. Our approach represents a significant step forward for raw EEG explainability and has the potential to provide many insights relating learned features and model predictions in future studies.

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Section: Resultsmentioning

confidence: 99%

Improving Multichannel Raw Electroencephalography-based Diagnosis of Major Depressive Disorder via Transfer Learning with Single Channel Sleep Stage Data*

Ellis

Miller

Calhoun

2023

Preprint

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“…Fuhl et al [111] suggested a system that achieved 92% dependability by combining ML techniques with a VOG strategy to improve eye-tracking accuracy. Zou and Zhang [112] proposed a novel methodology for eye Electrooculography decoding to achieve voiceless communication for patients with amyotrophic lateral sclerosis (ALS) using deep learning. The researchers utilized the deep Convolutional Neural Network (CNN) framework and proposed a deep CNN, called CNNword, for automating the decoding of EOG-based words, which can continuously analyse the dynamics within an EOG word by automatically learning stroke-to-character-to-VOLUME 4, 2016 word constructions.…”

Section: Eye-tracking and Artificial Intelligencementioning

confidence: 99%

Eye-Tracking Assistive Technologies for Individuals With Amyotrophic Lateral Sclerosis

et al. 2022

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Amyotrophic lateral sclerosis, also known as ALS, is a progressive nervous system disorder that affects nerve cells in the brain and spinal cord, resulting in the loss of muscle control. For individuals with ALS, where mobility is limited to the movement of the eyes, the use of eye-tracking-based applications can be applied to achieve some basic tasks with certain digital interfaces. This paper presents a review of existing eye-tracking software and hardware through which eye-tracking their application is sketched as an assistive technology to cope with ALS. Eye-tracking also provides a suitable alternative as control of game elements. Furthermore, artificial intelligence has been utilized to improve eye-tracking technology with significant improvement in calibration and accuracy. Gaps in literature are highlighted in the study to offer a direction for future research.

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“…These operations are carried out to make image datasets trainable with certain criteria. In this case, it directly increases the quality of the work done [34,35].…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning Based Convolutional Neural Network Structured New Image Classification Approach for Eye Disease Identification

Topaloğlu

2022

Scientia Iranica

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A deep learning-based convolutional artificial neural networks structured a new image classification method approach was implemented in the study. Sample application was carried out with Diabetic Retinopathy disease. Obtaining information about the blood vessels and any abnormal patterns from the rest of the phonoscopic image and assessing the degree of retinopathy is the problem itself. To solve this problem developed methodology and algorithmic structure of this new approach is presented in the study. An approach called care model was used in this study different from the classical CNN structure. The care approach is based on the idea that the best solution will be taken from the new data obtained by rescale the available data according to total number of pixels before the average data pool is created and then CNN processes will continue. In the care model approach, all data is multiplied by the number of elements by the number of epoch time eight tensors. The purposed care model includes VGG19 image classification model and developed mathematical model presented. Pre-trained model and all image dataset taken from kaggle and keras for implementation of case study. The purposed model provides train accuracy 87%, test accuracy 88%, precision 93% and recall 83%.

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eyeSay: Eye Electrooculography Decoding with Deep Learning

Cited by 10 publications

References 8 publications

Improving Multichannel Raw Electroencephalography-based Diagnosis of Major Depressive Disorder via Transfer Learning with Single Channel Sleep Stage Data*

Improving Multichannel Raw Electroencephalography-based Diagnosis of Major Depressive Disorder via Transfer Learning with Single Channel Sleep Stage Data*

Eye-Tracking Assistive Technologies for Individuals With Amyotrophic Lateral Sclerosis

Deep Learning Based Convolutional Neural Network Structured New Image Classification Approach for Eye Disease Identification

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