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20 pages, 538 KiB

Open AccessArticle

Exploring the Real-Time Variability and Complexity of Sitting Patterns in Office Workers with Non-Specific Chronic Spinal Pain and Pain-Free Individuals

by Eduarda Oliosi, Afonso Júlio, Phillip Probst, Luís Silva, João Paulo Vilas-Boas, Ana Rita Pinheiro and Hugo Gamboa

Sensors 2024, 24(14), 4750; https://doi.org/10.3390/s24144750 - 22 Jul 2024

Cited by 1 | Viewed by 1056

Abstract

Chronic spinal pain (CSP) is a prevalent condition, and prolonged sitting at work can contribute to it. Ergonomic factors like this can cause changes in motor variability. Variability analysis is a useful method to measure changes in motor performance over time. When performing [...] Read more.

Chronic spinal pain (CSP) is a prevalent condition, and prolonged sitting at work can contribute to it. Ergonomic factors like this can cause changes in motor variability. Variability analysis is a useful method to measure changes in motor performance over time. When performing the same task multiple times, different performance patterns can be observed. This variability is intrinsic to all biological systems and is noticeable in human movement. This study aims to examine whether changes in movement variability and complexity during real-time office work are influenced by CSP. The hypothesis is that individuals with and without pain will have different responses to office work tasks. Six office workers without pain and ten with CSP participated in this study. Participant’s trunk movements were recorded during work for an entire week. Linear and nonlinear measures of trunk kinematic displacement were used to assess movement variability and complexity. A mixed ANOVA was utilized to compare changes in movement variability and complexity between the two groups. The effects indicate that pain-free participants showed more complex and less predictable trunk movements with a lower degree of structure and variability when compared to the participants suffering from CSP. The differences were particularly noticeable in fine movements. Full article

(This article belongs to the Special Issue Biomedical Signal Processing and Health Monitoring Based on Sensors)

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16 pages, 3027 KiB

Open AccessArticle

Influence of Gestational Age on Pelvic Floor Muscle Activity, Plantar Contact, and Functional Mobility in High-Risk Pregnant Women: A Cross-Sectional Study

by Emilly Cássia Soares Furtado, Yury Souza De Azevedo, Deizyane dos Reis Galhardo, Iasmin Pereira Cabral Miranda, Maria Eunice Chagas Oliveira, Pablo Fabiano Moura das Neves, Lindinalva Brasil Monte, Erica Feio Carneiro Nunes, Elizabeth Alves Gonçalves Ferreira, Bianca Callegari, Givago da Silva Souza and João Simão de Melo-Neto

Sensors 2024, 24(14), 4615; https://doi.org/10.3390/s24144615 - 17 Jul 2024

Viewed by 916

Abstract

During pregnancy, biomechanical changes are observed due to hormonal and physical modifications, which can lead to alterations in the curvature of the spine, balance, gait patterns, and functionality of the pelvic floor muscles. This study aimed to investigate the progressive impact of biomechanical [...] Read more.

During pregnancy, biomechanical changes are observed due to hormonal and physical modifications, which can lead to alterations in the curvature of the spine, balance, gait patterns, and functionality of the pelvic floor muscles. This study aimed to investigate the progressive impact of biomechanical changes that occur during gestational weeks on the myoelectric activity of the pelvic floor muscles, plantar contact area, and functional mobility of high-risk pregnant women. Methods: This was a cross-sectional observational study carried out from November 2022 to March 2023. A total of 62 pregnant women of different gestational ages with high-risk pregnancies were analyzed using surface electromyography to assess the functionality of the pelvic floor muscles, plantigraphy (Staheli index and plantar contact area), and an accelerometer and gyroscope using the timed up and go test via an inertial sensor on a smartphone. Descriptive statistics and multivariate linear regression analyses were carried out to test the predictive value of the signature. Results: Increasing weeks of gestation resulted in a decrease in the RMS value (β = −0.306; t = −2.284; p = 0.026) according to the surface electromyography analyses. However, there was no association with plantar contact (F (4.50) = 0.697; p = 0.598; R² = 0.53). With regard to functional mobility, increasing weeks of gestation resulted in a decrease in time to standing (β = −0.613; t = −2.495; p = 0.016), time to go (β = −0.513; t = −2.264; p = 0.028), and first gyrus peak (β = −0.290; t = −2.168; p = 0.035). However, there was an increase in the time to come back (β = 0.453; t = 2.321; p = 0.025) as the number of gestational weeks increased. Conclusions: Increased gestational age is associated with a reduction in pelvic floor myoelectric activity. The plantar contact area did not change over the weeks. Advancing gestation was accompanied by a decrease in time to standing, time to go, and first gyrus peak, as well as an increase in time to come back. Full article

(This article belongs to the Special Issue Biomedical Signal Processing and Health Monitoring Based on Sensors)

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14 pages, 3425 KiB

Open AccessArticle

On-Road Evaluation of Unobtrusive In-Car Respiration Monitoring

by Adrian Radomski and Daniel Teichmann

Sensors 2024, 24(14), 4500; https://doi.org/10.3390/s24144500 - 11 Jul 2024

Viewed by 737

Abstract

This paper introduces and evaluates an innovative sensor for unobtrusive in-car respiration monitoring, mounted on the backrest of the driver’s seat. The sensor seamlessly integrates into the vehicle, measuring breathing rates continuously without requiring active participation from the driver. The paper proves the [...] Read more.

This paper introduces and evaluates an innovative sensor for unobtrusive in-car respiration monitoring, mounted on the backrest of the driver’s seat. The sensor seamlessly integrates into the vehicle, measuring breathing rates continuously without requiring active participation from the driver. The paper proves the feasibility of unobtrusive in-car measurements over long periods of time. Operation of the sensor was investigated over 12 participants sitting in the driver seat. A total of 107 min of driving in diverse conditions with overall coverage rate of 84.45% underscores the sensor potential to reliably capture physiological changes in breathing rate for fatigue and stress detection. Full article

(This article belongs to the Special Issue Biomedical Signal Processing and Health Monitoring Based on Sensors)

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19 pages, 3691 KiB

Open AccessArticle

Multimodal Sensing for Depression Risk Detection: Integrating Audio, Video, and Text Data

by Zhenwei Zhang, Shengming Zhang, Dong Ni, Zhaoguo Wei, Kongjun Yang, Shan Jin, Gan Huang, Zhen Liang, Li Zhang, Linling Li, Huijun Ding, Zhiguo Zhang and Jianhong Wang

Sensors 2024, 24(12), 3714; https://doi.org/10.3390/s24123714 - 7 Jun 2024

Cited by 3 | Viewed by 1986

Abstract

Depression is a major psychological disorder with a growing impact worldwide. Traditional methods for detecting the risk of depression, predominantly reliant on psychiatric evaluations and self-assessment questionnaires, are often criticized for their inefficiency and lack of objectivity. Advancements in deep learning have paved [...] Read more.

Depression is a major psychological disorder with a growing impact worldwide. Traditional methods for detecting the risk of depression, predominantly reliant on psychiatric evaluations and self-assessment questionnaires, are often criticized for their inefficiency and lack of objectivity. Advancements in deep learning have paved the way for innovations in depression risk detection methods that fuse multimodal data. This paper introduces a novel framework, the Audio, Video, and Text Fusion-Three Branch Network (AVTF-TBN), designed to amalgamate auditory, visual, and textual cues for a comprehensive analysis of depression risk. Our approach encompasses three dedicated branches—Audio Branch, Video Branch, and Text Branch—each responsible for extracting salient features from the corresponding modality. These features are subsequently fused through a multimodal fusion (MMF) module, yielding a robust feature vector that feeds into a predictive modeling layer. To further our research, we devised an emotion elicitation paradigm based on two distinct tasks—reading and interviewing—implemented to gather a rich, sensor-based depression risk detection dataset. The sensory equipment, such as cameras, captures subtle facial expressions and vocal characteristics essential for our analysis. The research thoroughly investigates the data generated by varying emotional stimuli and evaluates the contribution of different tasks to emotion evocation. During the experiment, the AVTF-TBN model has the best performance when the data from the two tasks are simultaneously used for detection, where the F1 Score is 0.78, Precision is 0.76, and Recall is 0.81. Our experimental results confirm the validity of the paradigm and demonstrate the efficacy of the AVTF-TBN model in detecting depression risk, showcasing the crucial role of sensor-based data in mental health detection. Full article

(This article belongs to the Special Issue Biomedical Signal Processing and Health Monitoring Based on Sensors)

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17 pages, 4976 KiB

Open AccessArticle

An Automatic Lie Detection Model Using EEG Signals Based on the Combination of Type 2 Fuzzy Sets and Deep Graph Convolutional Networks

by Mahsan Rahmani, Fatemeh Mohajelin, Nastaran Khaleghi, Sobhan Sheykhivand and Sebelan Danishvar

Sensors 2024, 24(11), 3598; https://doi.org/10.3390/s24113598 - 3 Jun 2024

Cited by 2 | Viewed by 1661

Abstract

In recent decades, many different governmental and nongovernmental organizations have used lie detection for various purposes, including ensuring the honesty of criminal confessions. As a result, this diagnosis is evaluated with a polygraph machine. However, the polygraph instrument has limitations and needs to [...] Read more.

In recent decades, many different governmental and nongovernmental organizations have used lie detection for various purposes, including ensuring the honesty of criminal confessions. As a result, this diagnosis is evaluated with a polygraph machine. However, the polygraph instrument has limitations and needs to be more reliable. This study introduces a new model for detecting lies using electroencephalogram (EEG) signals. An EEG database of 20 study participants was created to accomplish this goal. This study also used a six-layer graph convolutional network and type 2 fuzzy (TF-2) sets for feature selection/extraction and automatic classification. The classification results show that the proposed deep model effectively distinguishes between truths and lies. As a result, even in a noisy environment (SNR = 0 dB), the classification accuracy remains above 90%. The proposed strategy outperforms current research and algorithms. Its superior performance makes it suitable for a wide range of practical applications. Full article

(This article belongs to the Special Issue Biomedical Signal Processing and Health Monitoring Based on Sensors)

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14 pages, 1944 KiB

Open AccessArticle

Scented Solutions: Examining the Efficacy of Scent Interventions in Mitigating Driving Fatigue

by Xinyue Jiang, Kanesan Muthusamy, Jian Chen and Xueliang Fang

Sensors 2024, 24(8), 2384; https://doi.org/10.3390/s24082384 - 9 Apr 2024

Viewed by 1074

Abstract

Fatigued driving threatens the safety of people’s lives and property. Scent countermeasures offer minimal disruption and high efficacy, making them a promising approach. The aim of this study was to explore the application of scent countermeasures in alleviating fatigued driving. This study explored [...] Read more.

Fatigued driving threatens the safety of people’s lives and property. Scent countermeasures offer minimal disruption and high efficacy, making them a promising approach. The aim of this study was to explore the application of scent countermeasures in alleviating fatigued driving. This study explored changes in EEG frequency bands (alpha, beta, theta, and gamma) and the activity of EEG metrics (R_(α/β), R_θ_/(α+β) and R_{(α+θ)/(α+β)}) in the temporal lobe during driving tasks, selected fatigued driving identifiers, and aided validation by investigating subjective fatigue with the Karolinska Sleepiness Scale (KSS). The EEG indicators all increased, with a significant increase in R_(α/β). R_(α/β) was combined with the KSS to explore the effects of three scents, peppermint, grapefruit, and lavender, on driving fatigue. The subjective questionnaire results indicated that all three scents significantly improved driving fatigue, with significantly lower levels of driving fatigue compared to the control group. The analysis of EEG signals revealed a significant decrease in R_(α/β) after the implementation of scent countermeasures. Moreover, R_(α/β) was found to be lower in all three odor intervention groups compared to the control group. All three scents were found to significantly alleviate driving fatigue. The grapefruit scent had a better timely effect in relieving driving fatigue and the lavender scent had a longer effectiveness. This study provides further exploration for the application of odor interventions to alleviate driving fatigue. This study provides a practical reference for drivers to use odors to avoid fatigue in order to improve road safety. Full article

(This article belongs to the Special Issue Biomedical Signal Processing and Health Monitoring Based on Sensors)

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19 pages, 5977 KiB

Open AccessArticle

A Novel Approach for Automatic Detection of Driver Fatigue Using EEG Signals Based on Graph Convolutional Networks

by Sevda Zafarmandi Ardabili, Soufia Bahmani, Lida Zare Lahijan, Nastaran Khaleghi, Sobhan Sheykhivand and Sebelan Danishvar

Sensors 2024, 24(2), 364; https://doi.org/10.3390/s24020364 - 7 Jan 2024

Cited by 9 | Viewed by 2438

Abstract

Nowadays, the automatic detection of driver fatigue has become one of the important measures to prevent traffic accidents. For this purpose, a lot of research has been conducted in this field in recent years. However, the diagnosis of fatigue in recent research is [...] Read more.

Nowadays, the automatic detection of driver fatigue has become one of the important measures to prevent traffic accidents. For this purpose, a lot of research has been conducted in this field in recent years. However, the diagnosis of fatigue in recent research is binary and has no operational capability. This research presents a multi-class driver fatigue detection system based on electroencephalography (EEG) signals using deep learning networks. In the proposed system, a standard driving simulator has been designed, and a database has been collected based on the recording of EEG signals from 20 participants in five different classes of fatigue. In addition to self-report questionnaires, changes in physiological patterns are used to confirm the various stages of weariness in the suggested model. To pre-process and process the signal, a combination of generative adversarial networks (GAN) and graph convolutional networks (GCN) has been used. The proposed deep model includes five convolutional graph layers, one dense layer, and one fully connected layer. The accuracy obtained for the proposed model is 99%, 97%, 96%, and 91%, respectively, for the four different considered practical cases. The proposed model is compared to one developed through recent methods and research and has a promising performance. Full article

(This article belongs to the Special Issue Biomedical Signal Processing and Health Monitoring Based on Sensors)

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24 pages, 8390 KiB

Open AccessArticle

Salient Arithmetic Data Extraction from Brain Activity via an Improved Deep Network

by Nastaran Khaleghi, Shaghayegh Hashemi, Sevda Zafarmandi Ardabili, Sobhan Sheykhivand and Sebelan Danishvar

Sensors 2023, 23(23), 9351; https://doi.org/10.3390/s23239351 - 23 Nov 2023

Cited by 4 | Viewed by 1160

Abstract

Interpretation of neural activity in response to stimulations received from the surrounding environment is necessary to realize automatic brain decoding. Analyzing the brain recordings corresponding to visual stimulation helps to infer the effects of perception occurring by vision on brain activity. In this [...] Read more.

Interpretation of neural activity in response to stimulations received from the surrounding environment is necessary to realize automatic brain decoding. Analyzing the brain recordings corresponding to visual stimulation helps to infer the effects of perception occurring by vision on brain activity. In this paper, the impact of arithmetic concepts on vision-related brain records has been considered and an efficient convolutional neural network-based generative adversarial network (CNN-GAN) is proposed to map the electroencephalogram (EEG) to salient parts of the image stimuli. The first part of the proposed network consists of depth-wise one-dimensional convolution layers to classify the brain signals into 10 different categories according to Modified National Institute of Standards and Technology (MNIST) image digits. The output of the CNN part is fed forward to a fine-tuned GAN in the proposed model. The performance of the proposed CNN part is evaluated via the visually provoked 14-channel MindBigData recorded by David Vivancos, corresponding to images of 10 digits. An average accuracy of 95.4% is obtained for the CNN part for classification. The performance of the proposed CNN-GAN is evaluated based on saliency metrics of SSIM and CC equal to 92.9% and 97.28%, respectively. Furthermore, the EEG-based reconstruction of MNIST digits is accomplished by transferring and tuning the improved CNN-GAN’s trained weights. Full article

(This article belongs to the Special Issue Biomedical Signal Processing and Health Monitoring Based on Sensors)

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20 pages, 2284 KiB

Open AccessArticle

Instrumented Gait Classification Using Meaningful Features in Patients with Impaired Coordination

by Zeus T. Dominguez-Vega, Mariano Bernaldo de Quiros, Jan Willem J. Elting, Deborah A. Sival and Natasha M. Maurits

Sensors 2023, 23(20), 8410; https://doi.org/10.3390/s23208410 - 12 Oct 2023

Viewed by 1052

Abstract

Early onset ataxia (EOA) and developmental coordination disorder (DCD) both affect cerebellar functioning in children, making the clinical distinction challenging. We here aim to derive meaningful features from quantitative SARA-gait data (i.e., the gait test of the scale for the assessment and rating [...] Read more.

Early onset ataxia (EOA) and developmental coordination disorder (DCD) both affect cerebellar functioning in children, making the clinical distinction challenging. We here aim to derive meaningful features from quantitative SARA-gait data (i.e., the gait test of the scale for the assessment and rating of ataxia (SARA)) to classify EOA and DCD patients and typically developing (CTRL) children with better explainability than previous classification approaches. We collected data from 18 EOA, 14 DCD and 29 CTRL children, while executing both SARA gait tests. Inertial measurement units were used to acquire movement data, and a gait model was employed to derive meaningful features. We used a random forest classifier on 36 extracted features, leave-one-out-cross-validation and a synthetic oversampling technique to distinguish between the three groups. Classification accuracy, probabilities of classification and feature relevance were obtained. The mean classification accuracy was 62.9% for EOA, 85.5% for DCD and 94.5% for CTRL participants. Overall, the random forest algorithm correctly classified 82.0% of the participants, which was slightly better than clinical assessment (73.0%). The classification resulted in a mean precision of 0.78, mean recall of 0.70 and mean F1 score of 0.74. The most relevant features were related to the range of the hip flexion–extension angle for gait, and to movement variability for tandem gait. Our results suggest that classification, employing features representing different aspects of movement during gait and tandem gait, may provide an insightful tool for the differential diagnoses of EOA, DCD and typically developing children. Full article

(This article belongs to the Special Issue Biomedical Signal Processing and Health Monitoring Based on Sensors)

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23 pages, 8392 KiB

Open AccessArticle

Deep Learning for Detecting Multi-Level Driver Fatigue Using Physiological Signals: A Comprehensive Approach

by Mohammad Peivandi, Sevda Zafarmandi Ardabili, Sobhan Sheykhivand and Sebelan Danishvar

Sensors 2023, 23(19), 8171; https://doi.org/10.3390/s23198171 - 29 Sep 2023

Cited by 8 | Viewed by 2319

Abstract

A large share of traffic accidents is related to driver fatigue. In recent years, many studies have been organized in order to diagnose and warn drivers. In this research, a new approach was presented in order to detect multi-level driver fatigue. A multi-level [...] Read more.

A large share of traffic accidents is related to driver fatigue. In recent years, many studies have been organized in order to diagnose and warn drivers. In this research, a new approach was presented in order to detect multi-level driver fatigue. A multi-level driver tiredness diagnostic database based on physiological signals including ECG, EEG, EMG, and respiratory effort was developed for this aim. The EEG signal was used for processing and other recorded signals were used to confirm the driver’s fatigue so that fatigue was not confirmed based on self-report questionnaires. A customized architecture based on adversarial generative networks and convolutional neural networks (end-to-end) was utilized to select/extract features and classify different levels of fatigue. In the customized architecture, with the objective of eliminating uncertainty, type 2 fuzzy sets were used instead of activation functions such as Relu and Leaky Relu, and the performance of each was investigated. The final accuracy obtained in the three scenarios considered, two-level, three-level, and five-level, were 96.8%, 95.1%, and 89.1%, respectively. Given the suggested model’s optimal performance, which can identify five various levels of driver fatigue with high accuracy, it can be employed in practical applications of driver fatigue to warn drivers. Full article

(This article belongs to the Special Issue Biomedical Signal Processing and Health Monitoring Based on Sensors)

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11 pages, 1647 KiB

Open AccessArticle

Closed-Loop Auditory Stimulation to Guide Respiration: Preliminary Study to Evaluate the Effect on Time Spent in Sleep Initiation during a Nap

by Heenam Yoon and Sang Ho Choi

Sensors 2023, 23(14), 6468; https://doi.org/10.3390/s23146468 - 17 Jul 2023

Viewed by 1428

Abstract

Various stimulation systems to modulate sleep structure and function have been introduced. However, studies on the time spent in sleep initiation (TSSI) are limited. This study proposes a closed-loop auditory stimulation (CLAS) to gradually modulate respiratory rhythm linked to the autonomic nervous system [...] Read more.

Various stimulation systems to modulate sleep structure and function have been introduced. However, studies on the time spent in sleep initiation (TSSI) are limited. This study proposes a closed-loop auditory stimulation (CLAS) to gradually modulate respiratory rhythm linked to the autonomic nervous system (ANS) activity directly associated with sleep. CLAS is continuously updated to reflect the individual’s current respiratory frequency and pattern. Six participants took naps on different days with and without CLAS. The average values of the TSSI are 14.00 ± 4.24 and 9.67 ± 5.31 min in the control and stimulation experiments (p < 0.03), respectively. Further, the values of respiratory instability and heart rate variability differ significantly between the control and stimulation experiments. Based on our findings, CLAS supports the individuals to gradually modulate their respiratory rhythms to have similar characteristics observed near sleep initiation, and the changed respiratory rhythms influence ANS activities, possibly influencing sleep initiation. Our approach aims to modulate the respiratory rhythm, which can be controlled intentionally. Therefore, this method can probably be used for sleep initiation and daytime applications. Full article

(This article belongs to the Special Issue Biomedical Signal Processing and Health Monitoring Based on Sensors)

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16 pages, 6022 KiB

Open AccessArticle

Experimental Verification of the Possibility of Reducing Photoplethysmography Measurement Time for Stress Index Calculation

by Seung-Gun Lee, Young Do Song and Eui Chul Lee

Sensors 2023, 23(12), 5511; https://doi.org/10.3390/s23125511 - 12 Jun 2023

Cited by 4 | Viewed by 2038

Abstract

Stress is a direct or indirect cause of reduced work efficiency in daily life. It can damage physical and mental health, leading to cardiovascular disease and depression. With increased interest and awareness of the risks of stress in modern society, there is a [...] Read more.

Stress is a direct or indirect cause of reduced work efficiency in daily life. It can damage physical and mental health, leading to cardiovascular disease and depression. With increased interest and awareness of the risks of stress in modern society, there is a growing demand for quick assessment and monitoring of stress levels. Traditional ultra-short-term stress measurement classifies stress situations using heart rate variability (HRV) or pulse rate variability (PRV) information extracted from electrocardiogram (ECG) or photoplethysmography (PPG) signals. However, it requires more than one minute, making it difficult to monitor stress status in real-time and accurately predict stress levels. In this paper, stress indices were predicted using PRV indices acquired at different lengths of time (60 s, 50 s, 40 s, 30 s, 20 s, 10 s, and 5 s) for the purpose of real-time stress monitoring. Stress was predicted with Extra Tree Regressor, Random Forest Regressor, and Gradient Boost Regressor models using a valid PRV index for each data acquisition time. The predicted stress index was evaluated using an

R^{2}

score between the predicted stress index and the actual stress index calculated from one minute of the PPG signal. The average

R^{2}

score of the three models by the data acquisition time was 0.2194 at 5 s, 0.7600 at 10 s, 0.8846 at 20 s, 0.9263 at 30 s, 0.9501 at 40 s, 0.9733 at 50 s, and 0.9909 at 60 s. Thus, when stress was predicted using PPG data acquired for 10 s or more, the

R^{2}

score was confirmed to be over 0.7. Full article

(This article belongs to the Special Issue Biomedical Signal Processing and Health Monitoring Based on Sensors)

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15 pages, 5554 KiB

Open AccessArticle

Convolutional Neural Networks for the Real-Time Monitoring of Vital Signs Based on Impulse Radio Ultrawide-Band Radar during Sleep

by Sang Ho Choi and Heenam Yoon

Sensors 2023, 23(6), 3116; https://doi.org/10.3390/s23063116 - 14 Mar 2023

Cited by 12 | Viewed by 3676

Abstract

Vital signs provide important biometric information for managing health and disease, and it is important to monitor them for a long time in a daily home environment. To this end, we developed and evaluated a deep learning framework that estimates the respiration rate [...] Read more.

Vital signs provide important biometric information for managing health and disease, and it is important to monitor them for a long time in a daily home environment. To this end, we developed and evaluated a deep learning framework that estimates the respiration rate (RR) and heart rate (HR) in real time from long-term data measured during sleep using a contactless impulse radio ultrawide-band (IR-UWB) radar. The clutter is removed from the measured radar signal, and the position of the subject is detected using the standard deviation of each radar signal channel. The 1D signal of the selected UWB channel index and the 2D signal applied with the continuous wavelet transform are entered as inputs into the convolutional neural-network-based model that then estimates RR and HR. From 30 recordings measured during night-time sleep, 10 were used for training, 5 for validation, and 15 for testing. The average mean absolute errors for RR and HR were 2.67 and 4.78, respectively. The performance of the proposed model was confirmed for long-term data, including static and dynamic conditions, and it is expected to be used for health management through vital-sign monitoring in the home environment. Full article

(This article belongs to the Special Issue Biomedical Signal Processing and Health Monitoring Based on Sensors)

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14 pages, 11190 KiB

Open AccessArticle

Feature Pyramid Networks and Long Short-Term Memory for EEG Feature Map-Based Emotion Recognition

by Xiaodan Zhang, Yige Li, Jinxiang Du, Rui Zhao, Kemeng Xu, Lu Zhang and Yichong She

Sensors 2023, 23(3), 1622; https://doi.org/10.3390/s23031622 - 2 Feb 2023

Cited by 8 | Viewed by 2301

Abstract

The original EEG data collected are the 1D sequence, which ignores spatial topology information; Feature Pyramid Networks (FPN) is better at small dimension target detection and insufficient feature extraction in the scale transformation than CNN. We propose a method of FPN and Long [...] Read more.

The original EEG data collected are the 1D sequence, which ignores spatial topology information; Feature Pyramid Networks (FPN) is better at small dimension target detection and insufficient feature extraction in the scale transformation than CNN. We propose a method of FPN and Long Short-Term Memory (FPN-LSTM) for EEG feature map-based emotion recognition. According to the spatial arrangement of brain electrodes, the Azimuth Equidistant Projection (AEP) is employed to generate the 2D EEG map, which preserves the spatial topology information; then, the average power, variance power, and standard deviation power of three frequency bands (

α

,

β

, and

γ

) are extracted as the feature data for the EEG feature map. BiCubic interpolation is employed to interpolate the blank pixel among the electrodes; the three frequency bands EEG feature maps are used as the G, R, and B channels to generate EEG feature maps. Then, we put forward the idea of distributing the weight proportion for channels, assign large weight to strong emotion correlation channels (AF3, F3, F7, FC5, and T7), and assign small weight to the others; the proposed FPN-LSTM is used on EEG feature maps for emotion recognition. The experiment results show that the proposed method can achieve Value and Arousal recognition rates of 90.05% and 90.84%, respectively. Full article

(This article belongs to the Special Issue Biomedical Signal Processing and Health Monitoring Based on Sensors)

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15 pages, 673 KiB

Open AccessArticle

An Intelligent Motor Imagery Detection System Using Electroencephalography with Adaptive Wavelets

by Smith K. Khare, Nikhil Gaikwad and Neeraj Dhanraj Bokde

Sensors 2022, 22(21), 8128; https://doi.org/10.3390/s22218128 - 24 Oct 2022

Cited by 8 | Viewed by 2423

Abstract

Classification of motor imagery (MI) tasks provides a robust solution for specially-abled people to connect with the milieu for brain-computer interface. Precise selection of uniform tuning parameters of tunable Q wavelet transform (TQWT) for electroencephalography (EEG) signals is arduous. Therefore, this paper proposes [...] Read more.

Classification of motor imagery (MI) tasks provides a robust solution for specially-abled people to connect with the milieu for brain-computer interface. Precise selection of uniform tuning parameters of tunable Q wavelet transform (TQWT) for electroencephalography (EEG) signals is arduous. Therefore, this paper proposes robust TQWT for automatically selecting optimum tuning parameters to decompose non-stationary EEG signals accurately. Three evolutionary optimization algorithms are explored for automating the tuning parameters of robust TQWT. The fitness function of the mean square error of decomposition is used. This paper also exploits channel selection using a Laplacian score for dominant channel selection. Important features elicited from sub-bands of robust TQWT are classified using different kernels of the least square support vector machine classifier. The radial basis function kernel has provided the highest accuracy of 99.78%, proving that the proposed method is superior to other state-of-the-art using the same database. Full article

(This article belongs to the Special Issue Biomedical Signal Processing and Health Monitoring Based on Sensors)

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