This study investigated the potential of estimating various mental workload levels during two different tasks using a commercial in-ear electroencephalography (EEG) system, the IDUN 'Guardian'.Participants performed versions of two classical workload tasks: an n-back task and a mental arithmetic task. Both in-ear and conventional EEG data were simultaneously collected during these tasks. In an effort to facilitate a more comprehensive comparison, the complexity of the tasks was intentionally increased beyond typical levels. Special emphasis was also placed on understanding the significance ofband activity in workload estimations. Therefore, each signal was analyzed across low frequency (1-35 Hz) and high frequency (1-100 Hz) ranges. Additionally, surrogate in-ear EEG measures, derived from the conventional EEG recordings, were extracted and examined.Workload estimation using in-ear EEG yielded statistically significant performance levels, surpassing chance levels with 44.1% for four classes and 68.4% for two classes in the n-back task and was better than a naive predictor for the mental arithmetic task. Conventional EEG exhibited significantly higher performance compared to in-ear EEG, achieving 80.3% and 92.9% accuracy for the respective tasks, along with lower error rates than the naive predictor. The developed surrogate measures achieved improved results, reaching accuracies of 57.5% and 85.5%, thus providing insights for enhancing future in-ear systems. Notably, most high frequency range signals outperformed their low frequency counterparts in terms of accuracy validating that high frequencyband features can improve workload estimation.The application of EEG-based Brain-Computer Interfaces beyond laboratory settings is often hindered by practical limitations. In-ear EEG systems offer a promising solution to this problem, potentially enabling everyday use. This study evaluates the performance of a commercial in-ear headset and provides guidelines for increased effectiveness.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1088/1741-2552/ad8ef8 | DOI Listing |
Sleep Adv
November 2024
Department of Innovative Technologies, Institute of Digital Technologies for Personalized Healthcare (MeDiTech), University of Applied Sciences and Arts of Southern Switzerland, Lugano, Switzerland.
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 PDFSensors (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 PDFAdv 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 PDFJ 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.
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 PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!