Motion artifact contaminated multichannel EEG dataset.

Wearable EEG suffers from motion artifact contamination due to the subject's movement in an ambulatory environment. Signal processing techniques pose promising solutions for the detection and removal of motion artifacts from ambulatory EEG, but relevant open-access datasets are not available, which is detrimental to the development of wearable EEG applications. This article showcases open-access electroencephalography (EEG) recordings, while a subject is performing different upper-body, lower-body, and full-body movements.

Machine Learning Model for Computer-Aided Depression Screening among Young Adults Using Wireless EEG Headset.

Depression is a disorder that if not treated can hamper the quality of life. EEG has shown great promise in detecting depressed individuals from depression control individuals. It overcomes the limitations of traditional questionnaire-based methods.

Probability mapping based artifact detection and removal from single-channel EEG signals for brain-computer interface applications.

Background: Different types of artifacts in the electroencephalogram (EEG) signals can considerably reduce the performance of the later-stage EEG analysis algorithms for making decisions, such as those for brain-computer interfacing (BCI) classification. In this paper, we address the problem of artifact detection and removal from single-channel EEG signals.

New Method: We propose a novel approach that maps the probability of an EEG epoch to be artifactual based on four different statistical measures: entropy (a measure of uncertainty), kurtosis (a measure of peakedness), skewness (a measure of asymmetry), and periodic waveform index (a measure of periodicity).

Screening tools for early identification of children with developmental delay in low- and middle-income countries: a systematic review.

Objective: To systematically review, identify and report the screening tools used for early identification of developmental delay in low- and middle-income countries.

Design: Systematic review.

Data Sources: Four bibliographic databases: Medline (1946 to 13 July 2020), Embase (1974 to 13 July 2020), Scopus (1823 to 11 July 2020) and PsycINFO (1987 to July week 1 2020).

Emotion classification using single-channel scalp-EEG recording.

Several studies have found evidence for corticolimbic Theta electroencephalographic (EEG) oscillation in the neural processing of visual stimuli perceived as fear or threatening scene. Recent studies showed that neural oscillations' patterns in Theta, Alpha, Beta and Gamma sub-bands play a main role in brain's emotional processing. The main goal of this study is to classify two different emotional states by means of EEG data recorded through a single-electrode EEG headset.

Methods for artifact detection and removal from scalp EEG: A review.

Electroencephalography (EEG) is the most popular brain activity recording technique used in wide range of applications. One of the commonly faced problems in EEG recordings is the presence of artifacts that come from sources other than brain and contaminate the acquired signals significantly. Therefore, much research over the past 15 years has focused on identifying ways for handling such artifacts in the preprocessing stage.

A High Performance Delta-Sigma Modulator for Neurosensing.

Recorded neural data are frequently corrupted by large amplitude artifacts that are triggered by a variety of sources, such as subject movements, organ motions, electromagnetic interferences and discharges at the electrode surface. To prevent the system from saturating and the electronics from malfunctioning due to these large artifacts, a wide dynamic range for data acquisition is demanded, which is quite challenging to achieve and would require excessive circuit area and power for implementation. In this paper, we present a high performance Delta-Sigma modulator along with several design techniques and enabling blocks to reduce circuit area and power.

A Wavelet-Based Artifact Reduction From Scalp EEG for Epileptic Seizure Detection.

This paper presents a method to reduce artifacts from scalp EEG recordings to facilitate seizure diagnosis/detection for epilepsy patients. The proposed method is primarily based on stationary wavelet transform and takes the spectral band of seizure activities (i.e.

The Journey of Elastography: Background, Current Status, and Future Possibilities in Breast Cancer Diagnosis.

Elastography is a promising way to assess tissue differences regarding stiffness or elasticity for what was historically assessed manually by palpation. Combined with conventional imaging modalities (eg, ultrasonography [US]), elastography can potentially evaluate the stiffness of a breast lesion and consequently help to detect malignant breast tumor from benign ones. Recent studies show that ultrasonographic elastography (USE) provides higher image quality compared with conventional B-mode US or mammography during breast cancer diagnosis, which eventually helps to reduce false-positive results (ie, increased specificity) and therefore is useful in avoiding breast biopsy.

Artifact characterization and removal for in vivo neural recording.

Background: In vivo neural recordings are often corrupted by different artifacts, especially in a less-constrained recording environment. Due to limited understanding of the artifacts appeared in the in vivo neural data, it is more challenging to identify artifacts from neural signal components compared with other applications. The objective of this work is to analyze artifact characteristics and to develop an algorithm for automatic artifact detection and removal without distorting the signals of interest.

A bio-inspired cooperative algorithm for distributed source localization with mobile nodes.

In this paper we propose an algorithm for distributed optimization in mobile nodes. Compared with many published works, an important consideration here is that the nodes do not know the cost function beforehand. Instead of decision-making based on linear combination of the neighbor estimates, the proposed algorithm relies on information-rich nodes that are iteratively identified.

A 13µW 87dB dynamic range implantable ΔΣ modulator for full-spectrum neural recording.

Experiment analysis on in-vivo data sequences suggests a wide system dynamic range (DR) is required to simultaneously record local field potentials (LFPs), extra-cellular spikes, and artifacts/interferences. In this paper, we present a 13 µW 87 dB DR ΔΣ modulator for full-spectrum neural recording. To achieve a wide DR and low power consumption, a fully-differential topology is used with multi-bit (MB) quantization scheme and switched-opamp (SO) technique.