Decoding Visual Responses based on Deep Neural Networks with Ear-EEG Signals

Leel, Young-Eun; Lee, Minji

doi:10.1109/bci48061.2020.9061644

Cited by 14 publications

(7 citation statements)

References 40 publications

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“…Also, as we looked for minimal channel for practical/mobility, another factor to consider in these conditions is ambulatory. Works such as Lee and Lee ( 2020 ) have already shown that ML approach is more robust against a number of ambulatory conditions. Dataset also matters, as (Nakanishi et al, 2015 ; Ravi et al, 2020 ) already shown effects of different datasets used on performance evaluation.…”

Section: Discussionmentioning

confidence: 99%

A CNN-Based Deep Learning Approach for SSVEP Detection Targeting Binaural Ear-EEG

Israsena

Pan-ngum

2022

Front. Comput. Neurosci.

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This paper discusses a machine learning approach for detecting SSVEP at both ears with minimal channels. SSVEP is a robust EEG signal suitable for many BCI applications. It is strong at the visual cortex around the occipital area, but the SNR gets worse when detected from other areas of the head. To make use of SSVEP measured around the ears following the ear-EEG concept, especially for practical binaural implementation, we propose a CNN structure coupled with regressed softmax outputs to improve accuracy. Evaluating on a public dataset, we studied classification performance for both subject-dependent and subject-independent trainings. It was found that with the proposed structure using a group training approach, a 69.21% accuracy was achievable. An ITR of 6.42 bit/min given 63.49 % accuracy was recorded while only monitoring data from T7 and T8. This represents a 12.47% improvement from a single ear implementation and illustrates potential of the approach to enhance performance for practical implementation of wearable EEG.

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Section: Discussionmentioning

confidence: 99%

A CNN-Based Deep Learning Approach for SSVEP Detection Targeting Binaural Ear-EEG

Israsena

Pan-ngum

2022

Front. Comput. Neurosci.

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“…This review discusses such algorithms, which can also apply to other types of EEG systems. One such algorithm proposed by Lee and Lee [132] is a two-stream deep neural network that combines a CNN stream for extracting frequency-domain features and a long-short term memory stream for extracting time-domain features. The extracted features from both streams are then combined to map the classification output.…”

Section: Enhancing Ear-eeg Performance With Machine Learning and Sign...mentioning

confidence: 99%

The future of wearable EEG: a review of ear-EEG technology and its applications

Kaongoen,

Choi,

Woo Choi

et al. 2023

J. Neural Eng.

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Objective:This review paper provides a comprehensive overview of ear-EEG technology, which involves recording electroencephalogram (EEG) signals from electrodes placed in or around the ear, and its applications in the field of neural engineering.Approach:We conducted a thorough literature search using multiple databases to identify relevant studies related to ear-EEG technology and its various applications. We selected 123 publications and synthesized the information to highlight the main findings and trends in this field.Main Results:Our review highlights the potential of ear-EEG technology as the future of wearable EEG technology. We discuss the advantages and limitations of ear-EEG compared to traditional scalp-based EEG and methods to overcome those limitations. Through our review, we found that ear-EEG is a promising method that produces comparable results to conventional scalp-based methods. We review the development of ear-EEG sensing devices, including the design, types of sensors, and materials. We also review the current state of research on ear-EEG in different application areas such as brain-computer interfaces, and clinical monitoring.Significance:This review paper is the first to focus solely on reviewing ear-EEG research articles. As such, it serves as a valuable resource for researchers, clinicians, and engineers working in the field of neural engineering. Our review sheds light on the exciting future prospects of ear-EEG, and its potential to advance neural engineering research and become the future of wearable EEG technology.

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“…To widely spread practical BCI technology, we should consider the use of EEG in the real world. Several state-of-the-art BCI systems have demonstrated increased system performance using deep learning (Lee and Lee, 2020;Lee et al, 2020d;Mammone et al, 2020;Sun et al, 2020), but generally evaluate the system only in laboratory environments. However, some technical problems with external and internal artifacts have been addressed in real-world environments.…”

Section: Challengementioning

confidence: 99%

2020 International brain–computer interface competition: A review

Jeong

Cho²,

Lee³

et al. 2022

Front. Hum. Neurosci.

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The brain-computer interface (BCI) has been investigated as a form of communication tool between the brain and external devices. BCIs have been extended beyond communication and control over the years. The 2020 international BCI competition aimed to provide high-quality neuroscientific data for open access that could be used to evaluate the current degree of technical advances in BCI. Although there are a variety of remaining challenges for future BCI advances, we discuss some of more recent application directions: (i) few-shot EEG learning, (ii) micro-sleep detection (iii) imagined speech decoding, (iv) cross-session classification, and (v) EEG(+ear-EEG) detection in an ambulatory environment. Not only did scientists from the BCI field compete, but scholars with a broad variety of backgrounds and nationalities participated in the competition to address these challenges. Each dataset was prepared and separated into three data that were released to the competitors in the form of training and validation sets followed by a test set. Remarkable BCI advances were identified through the 2020 competition and indicated some trends of interest to BCI researchers.

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Decoding Visual Responses based on Deep Neural Networks with Ear-EEG Signals

Cited by 14 publications

References 40 publications

A CNN-Based Deep Learning Approach for SSVEP Detection Targeting Binaural Ear-EEG

A CNN-Based Deep Learning Approach for SSVEP Detection Targeting Binaural Ear-EEG

The future of wearable EEG: a review of ear-EEG technology and its applications

2020 International brain–computer interface competition: A review

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