Evaluating the Electroencephalographic Signal Quality of an In-Ear Wearable Device.

Sensors (Basel)

Laboratoire d'Imagerie Biomédicale (LIB) Inserm U1146, Sorbonne Université, UMR7371 CNRS, 15 Rue de l'Ecole de Medecine, 75006 Paris, France.

Published: June 2024

Wearable in-ear electroencephalographic (EEG) devices hold significant promise for advancing brain monitoring technologies into everyday applications. However, despite the current availability of several in-ear EEG devices in the market, there remains a critical need for robust validation against established clinical-grade systems. In this study, we carried out a detailed examination of the signal performance of a mobile in-ear EEG device from Naox Technologies. Our investigation had two main goals: firstly, evaluating the hardware circuit's reliability through simulated EEG signal experiments and, secondly, conducting a thorough comparison between the in-ear EEG device and gold-standard EEG monitoring equipment. This comparison assesses correlation coefficients with recognized physiological patterns during wakefulness and sleep, including alpha rhythms, eye artifacts, slow waves, spindles, and sleep stages. Our findings support the feasibility of using this in-ear EEG device for brain activity monitoring, particularly in scenarios requiring enhanced comfort and user-friendliness in various clinical and research settings.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11207223PMC
http://dx.doi.org/10.3390/s24123973DOI Listing

Publication Analysis

Top Keywords

in-ear eeg
16
eeg device
12
eeg devices
8
eeg
7
in-ear
6
evaluating electroencephalographic
4
electroencephalographic signal
4
signal quality
4
quality in-ear
4
in-ear wearable
4

Similar Publications

Study Objectives: Polysomnography (PSG) currently serves as the benchmark for evaluating sleep disorders. Its discomfort makes long-term monitoring unfeasible, leading to bias in sleep quality assessment. Hence, less invasive, cost-effective, and portable alternatives need to be explored.

View Article and Find Full Text PDF

Enhancing Deep Sleep Induction Through a Wireless In-Ear EEG Device Delivering Binaural Beats and ASMR: A Proof-of-Concept Study.

Sensors (Basel)

November 2024

Department of Mechanical and Mechatronics Engineering, University of Stellenbosch, Joubert Street, Stellenbosch 7602, South Africa.

This study presents the development of a wireless in-ear EEG device designed to monitor brain activity during sleep and deliver auditory stimuli aimed at enhancing deep sleep. The device records EEG signals and plays a combined auditory stimulus consisting of autonomous sensory meridian response (ASMR) and 3 Hz binaural beats at a 60:30 dB ratio, intended to promote delta wave activity and non-rapid eye movement (NREM) stage 3 sleep. Fifteen participants completed this study, which included two consecutive nights: a baseline night and a testing night.

View Article and Find Full Text PDF

In-Ear Electronics with Mechanical Adaptability for Physiological Sensing.

Adv Healthc Mater

December 2024

Department of Electrical and Computer Engineering, Queen's University, Kingston, ON, K7L 3N6, Canada.

Significant developments have been made in the field of wearable healthcare by utilizing soft materials for the construction of electronic sensors. However, the lack of adaptability to complex topologies, such as ear canal, results in inadequate sensing performance. Here, we report an in-ear physiological sensor with mechanical adaptability, which softens upon contact with the ear canal's skin, thus reducing the sensor-skin mechanical mismatch and interface impedance.

View Article and Find Full Text PDF

Multi-layer ear-scalp distillation framework for ear-EEG classification enhancement.

J Neural Eng

December 2024

School of Computer Science Technology, Beijing Institute of Technology, Beijing 100081, People's Republic of China.

Background: Ear-electroencephalography (ear-EEG) holds significant promise as a practical tool in brain-computer interfaces (BCIs) due to its enhanced unobtrusiveness, comfort, and mobility compared to traditional steady-state visual evoked potential (SSVEP)-based BCI systems. However, achieving accurate SSVEP classification with ear-EEG remains a major challenge due to the significant attenuation and distortion of the signal amplitude.

Objective: Our aim is to enhance the classification performance of SSVEP using ear-EEG and to increase its practical application value.

View Article and Find Full Text PDF

Auditory evoked potential electroencephalography-biometric dataset.

Data Brief

December 2024

Department of Electrical Engineering, École de Technologie Supérieure, Université du Québec, Canada.

This work aims to assess the use of electroencephalographic (EEG) signals as a means of biometric authentication. More than 240 recordings, each lasting 2 min, were gathered from 20 subjects involved in the data collection. Data include the results of experiments performed both in a resting state and in the presence of auditory stimuli.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!