The future of wearable EEG: A review of ear-EEG technology and its applications
The future of wearable EEG: A review of ear-EEG technology and its applications Page 1 Journal
of Neural Engineering ACCEPTED MANUSCRIPT The future of wearable EEG: A review of …
of Neural Engineering ACCEPTED MANUSCRIPT The future of wearable EEG: A review of …
Hearables, in-ear sensing devices for bio-signal acquisition: a narrative review
CKH Ne, J Muzaffar, A Amlani… - Expert Review of Medical …, 2021 - Taylor & Francis
Introduction Hearables are ear devices used for multiple purposes including
ubiquitous/remote monitoring of vital signals. This can support early detection, prevention …
ubiquitous/remote monitoring of vital signals. This can support early detection, prevention …
Continual learning of a transformer-based deep learning classifier using an initial model from action observation EEG data to online motor imagery classification
PL Lee, SH Chen, TC Chang, WK Lee, HT Hsu… - Bioengineering, 2023 - mdpi.com
The motor imagery (MI)-based brain computer interface (BCI) is an intuitive interface that
enables users to communicate with external environments through their minds. However …
enables users to communicate with external environments through their minds. However …
A novel online BCI system using speech imagery and ear-EEG for home appliances control
N Kaongoen, J Choi, S Jo - Computer Methods and Programs in …, 2022 - Elsevier
Abstract Background and Objective This paper investigates a novel way to interact with
home appliances via a brain-computer interface (BCI), using electroencephalograph (EEG) …
home appliances via a brain-computer interface (BCI), using electroencephalograph (EEG) …
Multiclass classification of imagined speech EEG using noise-assisted multivariate empirical mode decomposition and multireceptive field convolutional neural …
H Park, B Lee - Frontiers in human neuroscience, 2023 - frontiersin.org
Introduction In this study, we classified electroencephalography (EEG) data of imagined
speech using signal decomposition and multireceptive convolutional neural network. The …
speech using signal decomposition and multireceptive convolutional neural network. The …
[HTML][HTML] Neural decoding of imagined speech from EEG signals using the fusion of graph signal processing and graph learning techniques
Imagined Speech (IS) is the imagination of speech without using the tongue or muscles. In
recent studies, IS tasks are increasingly investigated for the Brain-Computer Interface (BCI) …
recent studies, IS tasks are increasingly investigated for the Brain-Computer Interface (BCI) …
ALIS: Learning affective causality behind daily activities from a wearable life-log system
Human emotions and behaviors are reciprocal components that shape each other in
everyday life. While the past research on each element has made use of various …
everyday life. While the past research on each element has made use of various …
Validation of cost-efficient EEG experimental setup for neural tracking in an auditory attention task
When individuals listen to speech, their neural activity phase-locks to the slow temporal
rhythm, which is commonly referred to as “neural tracking”. The neural tracking mechanism …
rhythm, which is commonly referred to as “neural tracking”. The neural tracking mechanism …
EEG-Oriented Self-Supervised Learning With Triple Information Pathways Network
Recently, deep learning-based electroencephalogram (EEG) analysis and decoding have
attracted widespread attention for monitoring the clinical condition of users and identifying …
attracted widespread attention for monitoring the clinical condition of users and identifying …
Decoding auditory-evoked response in affective states using wearable around-ear EEG system
J Choi, N Kaongoen, HS Choi, M Kim… - Biomedical Physics & …, 2023 - iopscience.iop.org
Objective. In this paper, an around-ear EEG system is investigated as an alternative
methodology to conventional scalp-EEG-based systems in classifying human affective states …
methodology to conventional scalp-EEG-based systems in classifying human affective states …