scholarly journals Detection of Negative Stress through Spectral Features of Electroencephalographic Recordings and a Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3050
Author(s):  
Arturo Martínez-Rodrigo ◽  
Beatriz García-Martínez ◽  
Álvaro Huerta ◽  
Raúl Alcaraz
Keyword(s):  
Learning Algorithm ◽  
Negative Impact ◽  
Brain Activity ◽  
Classification Performance ◽  
Emotional States ◽  
Physical And Mental Health ◽  
Eeg Signals ◽  
Deep Learning Algorithm ◽  
Power Spectral ◽  
Eeg Recordings

In recent years, electroencephalographic (EEG) signals have been intensively used in the area of emotion recognition, partcularly in distress identification due to its negative impact on physical and mental health. Traditionally, brain activity has been studied from a frequency perspective by computing the power spectral density of the EEG recordings and extracting features from different frequency sub-bands. However, these features are often individually extracted from single EEG channels, such that each brain region is separately evaluated, even when it has been corroborated that mental processes are based on the coordination of different brain areas working simultaneously. To take advantage of the brain’s behaviour as a synchronized network, in the present work, 2-D and 3-D spectral images constructed from common 32 channel EEG signals are evaluated for the first time to discern between emotional states of calm and distress using a well-known deep-learning algorithm, such as AlexNet. The obtained results revealed a significant improvement in the classification performance regarding previous works, reaching an accuracy about 84%. Moreover, no significant differences between the results provided by the diverse approaches considered to reconstruct 2-D and 3-D spectral maps from the original location of the EEG channels over the scalp were noticed, thus suggesting that these kinds of images preserve original spatial brain information.

scholarly journals Novel dry electrode EEG headbands for home use: Comparing performance and comfort

2020 ◽  
Vol 6 (3) ◽  
pp. 139-142
Author(s):  
Jens Haueisen ◽  
Patrique Fiedler ◽  
Anna Bernhardt ◽  
Ricardo Gonçalves ◽  
Carlos Fonseca
Keyword(s):  
Brain Activity ◽  
Skin Impedance ◽  
Signal Quality ◽  
The Novel ◽  
Application Time ◽  
Dry Electrodes ◽  
Power Spectral ◽  
Dry Electrode ◽  
Increasing Trend ◽  
Eeg Recordings

AbstractMonitoring brain activity at home using electroencephalography (EEG) is an increasing trend for both medical and non-medical applications. Gel-based electrodes are not suitable due to the gel application requiring extensive preparation and cleaning support for the patient or user. Dry electrodes can be applied without prior preparation by the patient or user. We investigate and compare two dry electrode headbands for EEG acquisition: a novel hybrid dual-textile headband comprising multipin and multiwave electrodes and a neoprene-based headband comprising hydrogel and spidershaped electrodes. We compare the headbands and electrodes in terms of electrode-skin impedance, comfort, electrode offset potential and EEG signal quality. We did not observe considerable differences in the power spectral density of EEG recordings. However, the hydrogel electrodes showed considerably increased impedances and offset potentials, limiting their compatibility with many EEG amplifiers. The hydrogel and spider-shaped electrodes required increased adduction, resulting in a lower wearing comfort throughout the application time compared to the novel headband comprising multipin and multiwave electrodes.

scholarly journals Detection of Hypoglycemia Using Measures of EEG Complexity in Type 1 Diabetes Patients

Entropy ◽  
2020 ◽  
Vol 22 (1) ◽  
pp. 81 ◽  
Author(s):  
Maria Rubega ◽  
Fabio Scarpa ◽  
Debora Teodori ◽  
Anne-Sophie Sejling ◽  
Christian S. Frandsen ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Single Channel ◽  
Permutation Entropy ◽  
Eeg Signals ◽  
Scalp Eeg ◽  
Power Spectral ◽  
Eeg Changes ◽  
Eeg Recordings ◽  
Clamp Study

Previous literature has demonstrated that hypoglycemic events in patients with type 1 diabetes (T1D) are associated with measurable scalp electroencephalography (EEG) changes in power spectral density. In the present study, we used a dataset of 19-channel scalp EEG recordings in 34 patients with T1D who underwent a hyperinsulinemic–hypoglycemic clamp study. We found that hypoglycemic events are also characterized by EEG complexity changes that are quantifiable at the single-channel level through empirical conditional and permutation entropy and fractal dimension indices, i.e., the Higuchi index, residuals, and tortuosity. Moreover, we demonstrated that the EEG complexity indices computed in parallel in more than one channel can be used as the input for a neural network aimed at identifying hypoglycemia and euglycemia. The accuracy was about 90%, suggesting that nonlinear indices applied to EEG signals might be useful in revealing hypoglycemic events from EEG recordings in patients with T1D.

A Prediction Approach Based on Self-Training and Deep Learning for Biological Data

2020 ◽  
Vol 10 (4) ◽  
pp. 50-64
Author(s):  
Mohamed Nadjib Boufenara ◽  
Mahmoud Boufaida ◽  
Mohamed Lamine Berkane
Keyword(s):  
Deep Learning ◽  
Supervised Learning ◽  
Learning Algorithm ◽  
Classification Performance ◽  
Biological Data ◽  
Unlabeled Data ◽  
Learning Methods ◽  
Deep Learning Algorithm ◽  
Prediction Approach ◽  
F Measure

With the exponential growth of biological data, labeling this kind of data becomes difficult and costly. Although unlabeled data are comparatively more plentiful than labeled ones, most supervised learning methods are not designed to use unlabeled data. Semi-supervised learning methods are motivated by the availability of large unlabeled datasets rather than a small amount of labeled examples. However, incorporating unlabeled data into learning does not guarantee an improvement in classification performance. This paper introduces an approach based on a model of semi-supervised learning, which is the self-training with a deep learning algorithm to predict missing classes from labeled and unlabeled data. In order to assess the performance of the proposed approach, two datasets are used with four performance measures: precision, recall, F-measure, and area under the ROC curve (AUC).

scholarly journals Multi-label classification of frog species via deep learning

2017 ◽  
Author(s):  
Jie Xie
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Single Species ◽  
Classification Performance ◽  
Time Frequency ◽  
Deep Learning Algorithm ◽  
Binary Relevance ◽  
Frog Species ◽  
Call Classification

Acoustic classification of frogs has received increasing attention for its promising application in ecological studies. Various studies have been proposed for classifying frog species, but most recordings are assumed to have only a single species. In this study, a method to classify multiple frog species in an audio clip is presented. To be specific, continuous frog recordings are first cropped into audio clips (10 seconds). Then, various time-frequency representations are generated for each 10-s recording. Next, instead of using traditional hand-crafted features, a deep learning algorithm is used to find the most important feature. Finally, a binary relevance based multi-label classification approach is proposed to classify simultaneously vocalizing frog species with our proposed features. Experimental results show that our proposed features extracted using deep learning can achieve better classification performance when compared to hand-crafted features for frog call classification.

scholarly journals Multi-label classification of frog species via deep learning

2017 ◽  
Author(s):  
Jie Xie
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Single Species ◽  
Classification Performance ◽  
Time Frequency ◽  
Deep Learning Algorithm ◽  
Binary Relevance ◽  
Frog Species ◽  
Call Classification

Acoustic classification of frogs has received increasing attention for its promising application in ecological studies. Various studies have been proposed for classifying frog species, but most recordings are assumed to have only a single species. In this study, a method to classify multiple frog species in an audio clip is presented. To be specific, continuous frog recordings are first cropped into audio clips (10 seconds). Then, various time-frequency representations are generated for each 10-s recording. Next, instead of using traditional hand-crafted features, a deep learning algorithm is used to find the most important feature. Finally, a binary relevance based multi-label classification approach is proposed to classify simultaneously vocalizing frog species with our proposed features. Experimental results show that our proposed features extracted using deep learning can achieve better classification performance when compared to hand-crafted features for frog call classification.

scholarly journals A New Signal Processing Approach for Discrimination of EEG Recordings

Stats ◽  
2018 ◽  
Vol 1 (1) ◽  
pp. 155-168 ◽  
Author(s):  
Hossein Hassani ◽  
Mohammad Yeganegi ◽  
Emmanuel Silva
Keyword(s):  
Principal Component Analysis ◽  
Brain Activity ◽  
Singular Spectrum Analysis ◽  
Principal Component ◽  
Discrete Wavelet ◽  
Brain Disorders ◽  
Eeg Signals ◽  
Eeg Recordings ◽  
Improving Accuracy ◽  
Electroencephalogram Eeg

Classifying brain activities based on electroencephalogram (EEG) signals is one of the important applications of time series discriminant analysis for diagnosing brain disorders. In this paper, we introduce a new method based on the Singular Spectrum Analysis (SSA) technique for classifying brain activity based on EEG signals via an application into a benchmark dataset for epileptic study with five categories, consisting of 100 EEG recordings per category. The results from the SSA based approach are xcompared with those from discrete wavelet transform before proposing a hybrid SSA and principal component analysis based approach for improving accuracy levels further.

scholarly journals Application of Electroencephalography-Based Machine Learning in Emotion Recognition: A Review

2021 ◽  
Vol 15 ◽  
Author(s):  
Jing Cai ◽  
Ruolan Xiao ◽  
Wenjie Cui ◽  
Shang Zhang ◽  
Guangda Liu
Keyword(s):  
Machine Learning ◽  
Emotion Recognition ◽  
Brain Activity ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Emotional States ◽  
Eeg Signals ◽  
Medical Field ◽  
External Stimuli

Emotion recognition has become increasingly prominent in the medical field and human-computer interaction. When people’s emotions change under external stimuli, various physiological signals of the human body will fluctuate. Electroencephalography (EEG) is closely related to brain activity, making it possible to judge the subject’s emotional changes through EEG signals. Meanwhile, machine learning algorithms, which are good at digging out data features from a statistical perspective and making judgments, have developed by leaps and bounds. Therefore, using machine learning to extract feature vectors related to emotional states from EEG signals and constructing a classifier to separate emotions into discrete states to realize emotion recognition has a broad development prospect. This paper introduces the acquisition, preprocessing, feature extraction, and classification of EEG signals in sequence following the progress of EEG-based machine learning algorithms for emotion recognition. And it may help beginners who will use EEG-based machine learning algorithms for emotion recognition to understand the development status of this field. The journals we selected are all retrieved from the Web of Science retrieval platform. And the publication dates of most of the selected articles are concentrated in 2016–2021.

Deep Learning-Based Diabetic Retinopathy Detection

2021 ◽  
Vol 11 (3) ◽  
pp. 38-48
Author(s):  
Mohamed Jebran P. ◽  
Sufia Banu
Keyword(s):  
Diabetes Mellitus ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Classification Performance ◽  
Learning Problems ◽  
Deep Learning Algorithm ◽  
Lesion Level ◽  
Fundus Photographs

Artificial intelligence (AI) is rapidly evolving from machine learning (ML) to deep learning (DL), which has ignited particular interest in ophthalmology as well. Deep learning has been applied in ophthalmology to fundus photographs, which achieve robust classification performance in the detection of diabetic retinopathy (DR). Diabetic retinopathy is a progressive condition observed in people who have had multiple years of diabetes mellitus. This paper focuses on examining how a deep learning algorithm can be applied for the detection and classification of diabetic retinopathy, both at the image level and at the lesion level. The performance of various neural networks is summarized by taking into account the sensitivity, precision, accuracy with respect to the size of the test datasets. Deep learning problems are discussed at the end.

scholarly journals An EEG Database and Its Initial Benchmark Emotion Classification Performance

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Ayan Seal ◽  
Puthi Prem Nivesh Reddy ◽  
Pingali Chaithanya ◽  
Arramada Meghana ◽  
Kamireddy Jahnavi ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Affective Computing ◽  
Industrial Applications ◽  
Classification Performance ◽  
Discrete Wavelet ◽  
Emotional States ◽  
Emotion Classification ◽  
Eeg Signals ◽  
Learning Machine ◽  
Facial Images

Human emotion recognition has been a major field of research in the last decades owing to its noteworthy academic and industrial applications. However, most of the state-of-the-art methods identified emotions after analyzing facial images. Emotion recognition using electroencephalogram (EEG) signals has got less attention. However, the advantage of using EEG signals is that it can capture real emotion. However, very few EEG signals databases are publicly available for affective computing. In this work, we present a database consisting of EEG signals of 44 volunteers. Twenty-three out of forty-four are females. A 32 channels CLARITY EEG traveler sensor is used to record four emotional states namely, happy, fear, sad, and neutral of subjects by showing 12 videos. So, 3 video files are devoted to each emotion. Participants are mapped with the emotion that they had felt after watching each video. The recorded EEG signals are considered further to classify four types of emotions based on discrete wavelet transform and extreme learning machine (ELM) for reporting the initial benchmark classification performance. The ELM algorithm is used for channel selection followed by subband selection. The proposed method performs the best when features are captured from the gamma subband of the FP1-F7 channel with 94.72% accuracy. The presented database would be available to the researchers for affective recognition applications.

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