Miniaturization for wearable EEG systems: recording hardware and data processing

Kim, Minjae; Yoo, So Young; Kim, Chul

doi:10.1007/s13534-022-00232-0

Cited by 6 publications

(4 citation statements)

References 112 publications

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“…However, recent research has shown that it is possible to construct EEG wearables, e.g. attached to the ear [79]. When these devices are available, they would complete the sensor set used in this work.…”

Section: A Future Directionsmentioning

confidence: 99%

Inferring Students’ Self-Assessed Concentration Levels in Daily Life Using Biosignal Data From Wearables

Södergård¹,

Laakko

2023

IEEE Access

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The ability to concentrate well is an important determinant of students' learning outcomes but remains poorly understood. In this work we investigated whether there exists a mapping between students' biosignals and perceived concentration levels. If we succeed in this mapping, a wearable can function as a Concentration Tracker, a novel feature that is missing from current wearables. For this, a wearable wristband was used to record students' heart rate, heart rate variability, skin temperature, skin conductivity and acceleration from body changes. Additionally, students self-assessed their concentration levels using a smartphone application. We improved the accuracy by utilizing a big amount of unlabelled biodata from outside the study sessions. Our best boosted regression tree model predicted students' concentration level with only 1.7% NMAE error. The predictions for a user not in the training set were much weaker; the best model, a convolutional neural network, achieved a prediction NMAE error of 30.7%. This implies that the users generated biosignals highly individually. Thus, models are not well transferable from one user to another without rooting them in user-specific data. Contrary to stress research, our results showed that skin conductivity had mostly a negative correlation with students' concentration levels. Also diverging from stress reactions, skin temperature had mainly a positive correlation. Conductivity and temperature were the two dominant predictors. Further, the results suggest that an element of deep, effortless concentration was present in the learning experience of the subjects. Altogether, our work demonstrates that a concentration tracking wearable for improving learning is technically achievable.

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Section: A Future Directionsmentioning

confidence: 99%

Inferring Students’ Self-Assessed Concentration Levels in Daily Life Using Biosignal Data From Wearables

Södergård¹,

Laakko

2023

IEEE Access

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“…In recent years, the practicality of wearable devices has become increasingly evident across numerous domains, such as industry, medicine, military, education, and entertainment [1][2][3]. Wearable devices designed for health monitoring purposes record various physiological signals, including electromyography (EMG) [4], electrocardiography (ECG) [5], electroencephalography (EEG) [6], and so on.…”

Section: Introductionmentioning

confidence: 99%

A feature enhanced EEG compression model using asymmetric encoding–decoding network ^*

Wang,

Zhang,

2024

J. Neural Eng.

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Objective. Recently, the demand for wearable devices using electroencephalography (EEG) has increased rapidly in many fields. Due to its volume and computation constraints, wearable devices usually compress and transmit EEG to external devices for analysis. However, current EEG compression algorithms are not tailor-made for wearable devices with limited computing and storage. Firstly, the huge amount of parameters makes it difficult to apply in wearable devices; secondly, it’s tricky to learn EEG signals’ distribution law due to the low signal-to-noise ratio, which leads to excessive reconstruction error and suboptimal compression performance. Approach. Here, a feature enhanced asymmetric encoding-decoding network is proposed. EEG is encoded with a lightweight model, and subsequently decoded with a multi-level feature fusion network by extracting the encoded features deeply and reconstructing the signal through a two-branch structure. Main results. On public EEG datasets, motor imagery and event-related potentials, experimental results show that the proposed method has achieved the state of the art compression performance. In addition, the neural representation analysis and the classification performance of the reconstructed EEG signals also show that our method tends to retain more task-related information as the compression ratio increases and retains reliable discriminative information after EEG compression. Significance. This paper tailors an asymmetric EEG compression method for wearable devices that achieves state-of-the-art compression performance in a lightweight manner, paving the way for the application of EEG-based wearable devices.

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“…In addition, individuals can use EEG to improve their productivity and wellness via monitoring their moods and emotions [ 25 ]. However, extracting meaningful information using few EEG channels in order to reduce the computational complexity of wearable headsets is still an ongoing challenge [ 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Machine Learning-Based Emotional Valence Recognition Approach Towards Wearable EEG

Abdel-Hamid

2023

Sensors

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Emotion artificial intelligence (AI) is being increasingly adopted in several industries such as healthcare and education. Facial expressions and tone of speech have been previously considered for emotion recognition, yet they have the drawback of being easily manipulated by subjects to mask their true emotions. Electroencephalography (EEG) has emerged as a reliable and cost-effective method to detect true human emotions. Recently, huge research effort has been put to develop efficient wearable EEG devices to be used by consumers in out of the lab scenarios. In this work, a subject-dependent emotional valence recognition method is implemented that is intended for utilization in emotion AI applications. Time and frequency features were computed from a single time series derived from the Fp1 and Fp2 channels. Several analyses were performed on the strongest valence emotions to determine the most relevant features, frequency bands, and EEG timeslots using the benchmark DEAP dataset. Binary classification experiments resulted in an accuracy of 97.42% using the alpha band, by that outperforming several approaches from literature by ~3–22%. Multiclass classification gave an accuracy of 95.0%. Feature computation and classification required less than 0.1 s. The proposed method thus has the advantage of reduced computational complexity as, unlike most methods in the literature, only two EEG channels were considered. In addition, minimal features concluded from the thorough analyses conducted in this study were used to achieve state-of-the-art performance. The implemented EEG emotion recognition method thus has the merits of being reliable and easily reproducible, making it well-suited for wearable EEG devices.

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Miniaturization for wearable EEG systems: recording hardware and data processing

Cited by 6 publications

References 112 publications

Inferring Students’ Self-Assessed Concentration Levels in Daily Life Using Biosignal Data From Wearables

Inferring Students’ Self-Assessed Concentration Levels in Daily Life Using Biosignal Data From Wearables

A feature enhanced EEG compression model using asymmetric encoding–decoding network ^*

An Efficient Machine Learning-Based Emotional Valence Recognition Approach Towards Wearable EEG

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Miniaturization for wearable EEG systems: recording hardware and data processing

Cited by 6 publications

References 112 publications

Inferring Students’ Self-Assessed Concentration Levels in Daily Life Using Biosignal Data From Wearables

Inferring Students’ Self-Assessed Concentration Levels in Daily Life Using Biosignal Data From Wearables

A feature enhanced EEG compression model using asymmetric encoding–decoding network *

An Efficient Machine Learning-Based Emotional Valence Recognition Approach Towards Wearable EEG

Contact Info

Product

Resources

About

A feature enhanced EEG compression model using asymmetric encoding–decoding network ^*