Pub Date : 2021-12-08DOI: 10.1109/BioSMART54244.2021.9677876
Lucia Cascone
Although it is well understood that changes in periocular features are linked to our brain activity, there is a paucity of research to analyze them in order to classify various cognitive processes. The present study investigates the effects on the periocular region of visual stimuli eliciting two different mental tasks: visual memory recall and understanding the semantic complexity of an image. The aim is to understand whether a subject is looking at an image with clear semantic content or not, or when he already knows the image that is shown to him. Based on these observations, the paper presents a study with the aim of demonstrating that the information that can be extracted from blinks, pupils and, gaze movements can potentially be used to classify people with respect to these two cognitive processes. Because there is a dearth of specialised research in this field, the encouraging results achieved in this study imply the necessity to generate specific datasets for this purpose.
{"title":"Periocular features as a window on cognitive processing: from memory to understanding the semantic complexity of an image","authors":"Lucia Cascone","doi":"10.1109/BioSMART54244.2021.9677876","DOIUrl":"https://doi.org/10.1109/BioSMART54244.2021.9677876","url":null,"abstract":"Although it is well understood that changes in periocular features are linked to our brain activity, there is a paucity of research to analyze them in order to classify various cognitive processes. The present study investigates the effects on the periocular region of visual stimuli eliciting two different mental tasks: visual memory recall and understanding the semantic complexity of an image. The aim is to understand whether a subject is looking at an image with clear semantic content or not, or when he already knows the image that is shown to him. Based on these observations, the paper presents a study with the aim of demonstrating that the information that can be extracted from blinks, pupils and, gaze movements can potentially be used to classify people with respect to these two cognitive processes. Because there is a dearth of specialised research in this field, the encouraging results achieved in this study imply the necessity to generate specific datasets for this purpose.","PeriodicalId":286026,"journal":{"name":"2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115309577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-08DOI: 10.1109/BioSMART54244.2021.9677765
B. Neji, Ndricim Ferko, I. Boulkaibet, Raymond Ghandour, Z. A. Barakeh, A. Karar
There has been an increased interest in detecting and identifying foot types. Foot plantar type detection has many utilization in various applications including human identification, footwear design, sports performance analysis, and injury prevention, and rehabilitation support systems. Determining plantar type is made possible by defining different anatomical classification of the foot that can be obtained by recording pressure points of the foot contact surface. In order to correctly predict a persons' plantar type, four different anatomical classes of foot were defined, simulated, and tested on a sensors' platform. The proposed system utilizes piezoelectric sensors that record pressure points of a subject's plantar foot. The collected data are processed in order to correctly define the subject plantar type.
{"title":"Plantar Type Identification Using Piezoelectric Pressure Sensors","authors":"B. Neji, Ndricim Ferko, I. Boulkaibet, Raymond Ghandour, Z. A. Barakeh, A. Karar","doi":"10.1109/BioSMART54244.2021.9677765","DOIUrl":"https://doi.org/10.1109/BioSMART54244.2021.9677765","url":null,"abstract":"There has been an increased interest in detecting and identifying foot types. Foot plantar type detection has many utilization in various applications including human identification, footwear design, sports performance analysis, and injury prevention, and rehabilitation support systems. Determining plantar type is made possible by defining different anatomical classification of the foot that can be obtained by recording pressure points of the foot contact surface. In order to correctly predict a persons' plantar type, four different anatomical classes of foot were defined, simulated, and tested on a sensors' platform. The proposed system utilizes piezoelectric sensors that record pressure points of a subject's plantar foot. The collected data are processed in order to correctly define the subject plantar type.","PeriodicalId":286026,"journal":{"name":"2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART)","volume":"382 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126726151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-08DOI: 10.1109/BioSMART54244.2021.9677569
Praveen K. Parashiva, A. Vinod
The human brain's response to mistakes or erroneous events is termed as Error-Related Potential (ErrP). The ErrP can be recorded non-invasively using Electroencephalogram (EEG). The ErrP activity is localized and gets reflected in a few EEG electrodes only. Further, EEG offers a poor signal-to-noise ratio. Therefore, single-trial detection of ErrP from EEG data is challenging. The objective of this work is to propose an efficient method for selecting electrodes that carry ErrP related information to enhance single-trial detection accuracy. In this work, the cosine similarity and Euclidian distance measures are used to rank the EEG electrodes. The selected top-ranked electrodes are used to extract electrode-average features followed by a classifier. This work is implemented on a public dataset containing 6 subjects' datasets each having 2 sessions of EEG data. The two proposed electrode ranking methods - cosine similarity measure and Euclidian distance measure are implemented separately. Both electrode ranking methods aided in achieving equally good ErrP detection rates. The cross-validated average detection rates achieved using the proposed electrode ranking methods are ~73.5% and ~80% for error and correct trials respectively. Further, the results are compared with three existing methods including Convolutional Neural Network (CNN) implemented on the same dataset used in this work to show the efficiency of the proposed method. The significance of this work is that the single-trial detection of ErrP can aid in improving the classification accuracy of decoding EEG tasks in Brain-Computer Interface systems.
{"title":"An Efficient Electrode Ranking Method for Single Trial Detection of EEG Error-Related Potentials","authors":"Praveen K. Parashiva, A. Vinod","doi":"10.1109/BioSMART54244.2021.9677569","DOIUrl":"https://doi.org/10.1109/BioSMART54244.2021.9677569","url":null,"abstract":"The human brain's response to mistakes or erroneous events is termed as Error-Related Potential (ErrP). The ErrP can be recorded non-invasively using Electroencephalogram (EEG). The ErrP activity is localized and gets reflected in a few EEG electrodes only. Further, EEG offers a poor signal-to-noise ratio. Therefore, single-trial detection of ErrP from EEG data is challenging. The objective of this work is to propose an efficient method for selecting electrodes that carry ErrP related information to enhance single-trial detection accuracy. In this work, the cosine similarity and Euclidian distance measures are used to rank the EEG electrodes. The selected top-ranked electrodes are used to extract electrode-average features followed by a classifier. This work is implemented on a public dataset containing 6 subjects' datasets each having 2 sessions of EEG data. The two proposed electrode ranking methods - cosine similarity measure and Euclidian distance measure are implemented separately. Both electrode ranking methods aided in achieving equally good ErrP detection rates. The cross-validated average detection rates achieved using the proposed electrode ranking methods are ~73.5% and ~80% for error and correct trials respectively. Further, the results are compared with three existing methods including Convolutional Neural Network (CNN) implemented on the same dataset used in this work to show the efficiency of the proposed method. The significance of this work is that the single-trial detection of ErrP can aid in improving the classification accuracy of decoding EEG tasks in Brain-Computer Interface systems.","PeriodicalId":286026,"journal":{"name":"2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART)","volume":"60 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114078332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-08DOI: 10.1109/BioSMART54244.2021.9677741
A. Roshdy, S. Alkork, A. Karar, H. Mhalla, T. Beyrouthy, Z. Al Barakeh, A. Nait-Ali
The primary objective of this research is the sta-tistical analysis of multi-channel electroencephalogram (EEG) signals for the purpose of emotion recognition performed in the valence-arousal space. The spatial information offered by the sensor location of the multi-channel EEG, is of critical importance as it does not only contain latent information, but also provides insights into the regions of the brain which are active during the expression of the targeted emotions. In particular, the linear correlation between the EEG channel features and the emotion value on the valence-arousal axes is obtained over different frequency ranges using the Pearson method. The five different features utilized in this study are the power of each sensor, power difference between symmetric sensors, power ratio between symmetric differences, average of the sensors readings, and standard deviation of the sensors readings. The statistical analysis was performed using the standard DEAP data set valence, arousal, and dominance values along with raw multi-channel EEG data. Preliminary results indicate that it is possible to optimize the number of sensors used in capturing the EEG signal, while maintaining a high degree of emotion detection accuracy. The standard deviation was found to be the most optimum metric for detecting valence emotion, while the beta frequency range is the better suited for detecting arousal with any of the devised metrics.
{"title":"Statistical Analysis of Multi-channel EEG Signals for Digitizing Human Emotions","authors":"A. Roshdy, S. Alkork, A. Karar, H. Mhalla, T. Beyrouthy, Z. Al Barakeh, A. Nait-Ali","doi":"10.1109/BioSMART54244.2021.9677741","DOIUrl":"https://doi.org/10.1109/BioSMART54244.2021.9677741","url":null,"abstract":"The primary objective of this research is the sta-tistical analysis of multi-channel electroencephalogram (EEG) signals for the purpose of emotion recognition performed in the valence-arousal space. The spatial information offered by the sensor location of the multi-channel EEG, is of critical importance as it does not only contain latent information, but also provides insights into the regions of the brain which are active during the expression of the targeted emotions. In particular, the linear correlation between the EEG channel features and the emotion value on the valence-arousal axes is obtained over different frequency ranges using the Pearson method. The five different features utilized in this study are the power of each sensor, power difference between symmetric sensors, power ratio between symmetric differences, average of the sensors readings, and standard deviation of the sensors readings. The statistical analysis was performed using the standard DEAP data set valence, arousal, and dominance values along with raw multi-channel EEG data. Preliminary results indicate that it is possible to optimize the number of sensors used in capturing the EEG signal, while maintaining a high degree of emotion detection accuracy. The standard deviation was found to be the most optimum metric for detecting valence emotion, while the beta frequency range is the better suited for detecting arousal with any of the devised metrics.","PeriodicalId":286026,"journal":{"name":"2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124528679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-08DOI: 10.1109/BioSMART54244.2021.9677801
Raymond Ghandour, A. Potams, I. Boulkaibet, B. Neji, Z. A. Barakeh, A. Karar
Driver behaviour detection and evaluation is becoming an essential task for vehicle manufacturers. Driver distraction is the major cause of road accidents and infrastructure deformation. Furthermore, secondary roads accidents are mainly affected, since external distraction and pedestrian presence are higher than highways. In this paper, we propose a comparison of three machine learning classification methods to identify the driver's behaviour on secondary roads. The classification and comparison are based on the evaluation of real data.
{"title":"Machine learning methods for driver behaviour classification","authors":"Raymond Ghandour, A. Potams, I. Boulkaibet, B. Neji, Z. A. Barakeh, A. Karar","doi":"10.1109/BioSMART54244.2021.9677801","DOIUrl":"https://doi.org/10.1109/BioSMART54244.2021.9677801","url":null,"abstract":"Driver behaviour detection and evaluation is becoming an essential task for vehicle manufacturers. Driver distraction is the major cause of road accidents and infrastructure deformation. Furthermore, secondary roads accidents are mainly affected, since external distraction and pedestrian presence are higher than highways. In this paper, we propose a comparison of three machine learning classification methods to identify the driver's behaviour on secondary roads. The classification and comparison are based on the evaluation of real data.","PeriodicalId":286026,"journal":{"name":"2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART)","volume":"307 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115999907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-08DOI: 10.1109/BioSMART54244.2021.9677700
Saad B. Younis, E. Al-Hemiary
Virtual reality (VR) is a virtual environment that might be identical to or diametrically opposed to the actual world. The traditional learning method for surgical training such that Cadaver Surgery is an effective method proven in the medical field to understand human anatomy and to perform training surgery; when held in comparison to digital 3D models (i.e., VR), it tends to be more complex, more expensive, and other concerned safety concern it. To teach various medical groups, VR is a modern approach that helps residents, students, and professionals in various fields of medicine grasp complex operations such as total hip replacement surgery before carrying them out on a patient. The VR application used an Oculus Quest headset with two hand controller; in this VR application, the user performs total hip joint replacement surgery procedures using a two-stage skeleton and total body's organ. the virtual reality application received a System Usability Scale score of (85.444), indicating that the application is recommended and good according to the System Usability Scale range. Also, for the virtual reality application, some participants in quantitative assessment got improvements of more than 50%, which is a positive sign. according to the results of this study, the feedback was positive. Based on the final result of this paper, it is that incorporating virtual reality skills into various medical teams might help them perform better in general and more specifically in Surgical procedures. Therefore, it is feasible to suggest that VR applications can train different medical groups to improve their skills in surgical procedures.
{"title":"Immersive Virtual Reality Application For Total Hip Replacement Surgical Training","authors":"Saad B. Younis, E. Al-Hemiary","doi":"10.1109/BioSMART54244.2021.9677700","DOIUrl":"https://doi.org/10.1109/BioSMART54244.2021.9677700","url":null,"abstract":"Virtual reality (VR) is a virtual environment that might be identical to or diametrically opposed to the actual world. The traditional learning method for surgical training such that Cadaver Surgery is an effective method proven in the medical field to understand human anatomy and to perform training surgery; when held in comparison to digital 3D models (i.e., VR), it tends to be more complex, more expensive, and other concerned safety concern it. To teach various medical groups, VR is a modern approach that helps residents, students, and professionals in various fields of medicine grasp complex operations such as total hip replacement surgery before carrying them out on a patient. The VR application used an Oculus Quest headset with two hand controller; in this VR application, the user performs total hip joint replacement surgery procedures using a two-stage skeleton and total body's organ. the virtual reality application received a System Usability Scale score of (85.444), indicating that the application is recommended and good according to the System Usability Scale range. Also, for the virtual reality application, some participants in quantitative assessment got improvements of more than 50%, which is a positive sign. according to the results of this study, the feedback was positive. Based on the final result of this paper, it is that incorporating virtual reality skills into various medical teams might help them perform better in general and more specifically in Surgical procedures. Therefore, it is feasible to suggest that VR applications can train different medical groups to improve their skills in surgical procedures.","PeriodicalId":286026,"journal":{"name":"2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128695833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-08DOI: 10.1109/BioSMART54244.2021.9677840
Israel Raul Tiñini Alvarez, Guillermo Sahonero-Alvarez, Carlos Menacho, Josmar Suarez
Gait Recognition, as a way to identify people, is re-markably attractive for scenarios in which it is not possible to rely on subjects' collaboration. Nevertheless, from all the modalities that Gait Recognition involve, vision-based approaches are better to meet hardware and settings-limitations. Because of that, in the past years, there has been several efforts on developing robust algorithms against visual gait covariates, i.e., view, clothing and carrying variations. However, besides robustness, real-world gait recognition systems also require to be implemented considering near real-time computational demands as well as portability. In this work we propose an Edge Computing approach based on the NVIDIA Jetson Nano development board and the OpenCV OAK-D camera to perform Gait Recognition. To adapt our approach, we created two small data sets that allowed our system to particularize the system to local data. Our pipeline implies the usage of a pre-trained object detection algorithm in the OAK-D, and the execution of both the representation extraction and inference on the Jetson Nano. To test our framework, we first explore its feasibility and consistency in an offline manner. Later, we characterize the complexity and time processing when executing the procedures in an online setup. Our results show that the approach is promising as it allows online operation with an inference time of 35.8 ms.
{"title":"Exploring Edge Computing for Gait Recognition","authors":"Israel Raul Tiñini Alvarez, Guillermo Sahonero-Alvarez, Carlos Menacho, Josmar Suarez","doi":"10.1109/BioSMART54244.2021.9677840","DOIUrl":"https://doi.org/10.1109/BioSMART54244.2021.9677840","url":null,"abstract":"Gait Recognition, as a way to identify people, is re-markably attractive for scenarios in which it is not possible to rely on subjects' collaboration. Nevertheless, from all the modalities that Gait Recognition involve, vision-based approaches are better to meet hardware and settings-limitations. Because of that, in the past years, there has been several efforts on developing robust algorithms against visual gait covariates, i.e., view, clothing and carrying variations. However, besides robustness, real-world gait recognition systems also require to be implemented considering near real-time computational demands as well as portability. In this work we propose an Edge Computing approach based on the NVIDIA Jetson Nano development board and the OpenCV OAK-D camera to perform Gait Recognition. To adapt our approach, we created two small data sets that allowed our system to particularize the system to local data. Our pipeline implies the usage of a pre-trained object detection algorithm in the OAK-D, and the execution of both the representation extraction and inference on the Jetson Nano. To test our framework, we first explore its feasibility and consistency in an offline manner. Later, we characterize the complexity and time processing when executing the procedures in an online setup. Our results show that the approach is promising as it allows online operation with an inference time of 35.8 ms.","PeriodicalId":286026,"journal":{"name":"2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122195528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-08DOI: 10.1109/BioSMART54244.2021.9677748
Rateb Katmah, Fares Al-Shargie, U. Tariq, F. Babiloni, Fadwa Al-Mughairbi, H. Al-Nashash
Stress is a major cause of many mental, psychological, emotional, behavioral, and physical disorders. Therefore, early detection of stress can help prevent many ailments and improve human health. In this study, we used a modified Stroop Color Word Task (SCWT) with time pressure and negative feedback to elicit two levels of stress at the workplace. We then assessed the level of stress using functional near-infrared spectroscopy (fNIRS) with multiple machine learning classifiers. We analyzed the fNIRS signals using partial directed coherence (PDC) to estimate the effective connectivity network between brain regions under stress. Our results showed that the proposed stress task reduced the cognitive performance and altered the connectivity network on the frontal region. The left frontal and left dorsolateral regions showed significantly higher connectivity under stress, p<0.05. Meanwhile, the right ventrolateral prefrontal cortex (VLPFC) showed a significant decrease in the connectivity network under stress. We achieved the highest classification performance using support vector machine (SVM) with an average classification accuracy of 99.93%. Our results highlight using fNIRS with PDC at the frontal brain region as a potential biomarker for stress.
{"title":"Connectivity Analysis under Mental Stress using fNIRS","authors":"Rateb Katmah, Fares Al-Shargie, U. Tariq, F. Babiloni, Fadwa Al-Mughairbi, H. Al-Nashash","doi":"10.1109/BioSMART54244.2021.9677748","DOIUrl":"https://doi.org/10.1109/BioSMART54244.2021.9677748","url":null,"abstract":"Stress is a major cause of many mental, psychological, emotional, behavioral, and physical disorders. Therefore, early detection of stress can help prevent many ailments and improve human health. In this study, we used a modified Stroop Color Word Task (SCWT) with time pressure and negative feedback to elicit two levels of stress at the workplace. We then assessed the level of stress using functional near-infrared spectroscopy (fNIRS) with multiple machine learning classifiers. We analyzed the fNIRS signals using partial directed coherence (PDC) to estimate the effective connectivity network between brain regions under stress. Our results showed that the proposed stress task reduced the cognitive performance and altered the connectivity network on the frontal region. The left frontal and left dorsolateral regions showed significantly higher connectivity under stress, p<0.05. Meanwhile, the right ventrolateral prefrontal cortex (VLPFC) showed a significant decrease in the connectivity network under stress. We achieved the highest classification performance using support vector machine (SVM) with an average classification accuracy of 99.93%. Our results highlight using fNIRS with PDC at the frontal brain region as a potential biomarker for stress.","PeriodicalId":286026,"journal":{"name":"2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127582856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-08DOI: 10.1109/biosmart54244.2021.9677783
{"title":"BioSMART 2021 Proceedings","authors":"","doi":"10.1109/biosmart54244.2021.9677783","DOIUrl":"https://doi.org/10.1109/biosmart54244.2021.9677783","url":null,"abstract":"","PeriodicalId":286026,"journal":{"name":"2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART)","volume":"18 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132365349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-08DOI: 10.1109/BioSMART54244.2021.9677866
Vincent Le Du, Charley Presigny, Arabella Bouzigues, V. Godefroy, B. Batrancourt, R. Levy, F. De Vico Fallani, R. Migliaccio
Multilayer networks (MNs) constitute an elegant and insightful multidimensional or multimodal framework. Bimodal MNs made from brain functional and structural networks extracted from neuroimaging modalities commonly lay the ground for truly emergent multimodal analysis. Thus far, they are computed using the same atlas for both layers. However, different atlases are required for specific imaging modalities. Depending on which atlas is chosen for a specific modality, this can lead to information from the other modalities being compromised. In this paper, we propose a new way to build such networks using specific atlases suited to each modality. The new technique is based on the computation of spatial overlaps between regions from different parcellations used for each available modality. We generalized the multiplex core-periphery method used to distinguish core and peripheral brain regions to apply it to such MNs, and to evaluate the approach and compare it to previous versions. We applied this new method in behavioral variant frontotemporal dementia (bvFTD) patients and healthy controls. First, we chose two specific atlases, the AAL2 and Schaefer100-Yeo17, for our DWI and fMRI data respectively. Subsequently, we computed richness and coreness for each subject. Finally, we benchmarked our results to evaluate the technique. We obtained higher peaks of significance and Fishers Criterion than with the previous method in the conditions that replicates previous findings. This highlights the potential of our multi-atlas MNs as well as their usefulness in MN analysis.
{"title":"Multi-atlas Multilayer Brain Networks, a new multimodal approach to neurodegenerative disease","authors":"Vincent Le Du, Charley Presigny, Arabella Bouzigues, V. Godefroy, B. Batrancourt, R. Levy, F. De Vico Fallani, R. Migliaccio","doi":"10.1109/BioSMART54244.2021.9677866","DOIUrl":"https://doi.org/10.1109/BioSMART54244.2021.9677866","url":null,"abstract":"Multilayer networks (MNs) constitute an elegant and insightful multidimensional or multimodal framework. Bimodal MNs made from brain functional and structural networks extracted from neuroimaging modalities commonly lay the ground for truly emergent multimodal analysis. Thus far, they are computed using the same atlas for both layers. However, different atlases are required for specific imaging modalities. Depending on which atlas is chosen for a specific modality, this can lead to information from the other modalities being compromised. In this paper, we propose a new way to build such networks using specific atlases suited to each modality. The new technique is based on the computation of spatial overlaps between regions from different parcellations used for each available modality. We generalized the multiplex core-periphery method used to distinguish core and peripheral brain regions to apply it to such MNs, and to evaluate the approach and compare it to previous versions. We applied this new method in behavioral variant frontotemporal dementia (bvFTD) patients and healthy controls. First, we chose two specific atlases, the AAL2 and Schaefer100-Yeo17, for our DWI and fMRI data respectively. Subsequently, we computed richness and coreness for each subject. Finally, we benchmarked our results to evaluate the technique. We obtained higher peaks of significance and Fishers Criterion than with the previous method in the conditions that replicates previous findings. This highlights the potential of our multi-atlas MNs as well as their usefulness in MN analysis.","PeriodicalId":286026,"journal":{"name":"2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122891083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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