EEG signal processing techniques and applications

Editoria

A Multi-Modal Analysis of the Freezing of Gait Phenomenon in Parkinson’s Disease

Background: Freezing of Gait (FOG) is one of the most disabling motor complications of Parkinson’s disease, and consists of an episodic inability to move forward, despite the intention to walk. FOG increases the risk of falls and reduces the quality of life of patients and their caregivers. The phenomenon is difficult to appreciate during outpatients visits; hence, its automatic recognition is of great clinical importance. Many types of sensors and different locations on the body have been proposed. However, the advantages of a multi-sensor configuration with respect to a single-sensor one are not clear, whereas this latter would be advisable for use in a non-supervised environment. Methods: In this study, we used a multi-modal dataset and machine learning algorithms to perform different classifications between FOG and non-FOG periods. Moreover, we explored the relevance of features in the time and frequency domains extracted from inertial sensors, electroencephalogram and skin conductance. We developed both a subject-independent and a subject-dependent algorithm, considering different sensor subsets. Results: The subject-independent and subject-dependent algorithms yielded accuracies of 85% and 88% in the leave-one-subject-out and leave-one-task-out test, respectively. Results suggest that the inertial sensors positioned on the lower limb are generally the most significant in recognizing FOG. Moreover, the performance impairment experienced when using a single tibial accelerometer instead of the optimal multi-modal configuration is limited to 2–3%. Conclusions: The achieved results disclose the possibility of getting a good FOG recognition using a minimally invasive set-up made of a single inertial sensor. This is very significant in the perspective of implementing a long-term monitoring of patients in their homes, during activities of daily living

Editorial: New theories, models, and AI methods of brain dynamics, brain decoding and neuromodulation

The human brain is highly dynamic and complex, supporting a remarkable range of functions by dynamically integrating and coordinating different brain regions and networks across multiple spatial and temporal scales. Research on the human brain has become truly interdisciplinary involving medicine, neurobiology, engineering, and related fields. A thorough understanding of the mechanisms of neuromodulation actions is urgently needed for stimulation parameters optimization, response prediction, and consistent therapy. This Research Topic aims to combine top-down and bottom-up methods to produce robust results that allow for a meaningful interpretation in terms of the underlying brain dynamics with an emphasis on brain decoding and neuromodulation

A Multi-Modal Analysis of the Freezing of Gait Phenomenon in Parkinson’s Disease

Background: Freezing of Gait (FOG) is one of the most disabling motor complications of Parkinson’s disease, and consists of an episodic inability to move forward, despite the intention to walk. FOG increases the risk of falls and reduces the quality of life of patients and their caregivers. The phenomenon is difficult to appreciate during outpatients visits; hence, its automatic recognition is of great clinical importance. Many types of sensors and different locations on the body have been proposed. However, the advantages of a multi-sensor configuration with respect to a single-sensor one are not clear, whereas this latter would be advisable for use in a non-supervised environment. Methods: In this study, we used a multi-modal dataset and machine learning algorithms to perform different classifications between FOG and non-FOG periods. Moreover, we explored the relevance of features in the time and frequency domains extracted from inertial sensors, electroencephalogram and skin conductance. We developed both a subject-independent and a subject-dependent algorithm, considering different sensor subsets. Results: The subject-independent and subject-dependent algorithms yielded accuracies of 85% and 88% in the leave-one-subject-out and leave-one-task-out test, respectively. Results suggest that the inertial sensors positioned on the lower limb are generally the most significant in recognizing FOG. Moreover, the performance impairment experienced when using a single tibial accelerometer instead of the optimal multi-modal configuration is limited to 2–3%. Conclusions: The achieved results disclose the possibility of getting a good FOG recognition using a minimally invasive set-up made of a single inertial sensor. This is very significant in the perspective of implementing a long-term monitoring of patients in their homes, during activities of daily living

A brief discussion on the interference and elimination of rainfall to the water pipe inclinometer at Fengning station

Observation data of DSQ water pipe inclinometer and meteorology at Fengning seismic station from 2021 to 2023 were selected. Correlation analysis and convolution filter analysis were performed on rainfall and observation data, and convolution filter method and regression analysis were used to eliminate the lag effect of rainfall, and the relationship between rainfall and water pipe stress variables was judged. The results show that the trend change of the Fengning DSQ water pipe inclinometer was related to rainfall, and the daily variation of the observed data had a good linear relationship with the rainfall accumulation value, but did not obviously correlate with the instantaneous rainfall, and the response time had a lag of 1 day. Convolution filtering can better eliminate the step change caused by rainfall, but it failed to completely eliminate the southward inclination of NS and eastward inclination of EW after the rainfall of the water pipe meter from June to September 2021. Other factors may also affect the inclination of NS to S and EW to E of the water pipe meter

Notch1 promotes resistance to cisplatin by up-regulating Ecto-5'-nucleotidase (CD73) in triple-negative breast cancer cells

Triple-negative breast cancer (TNBC) is an aggressive molecular subtype that due to lack of druggable targets is treated with chemotherapy as standard of care. However, TNBC is prone to chemoresistance and associates with poor survival. The aim of this study was to explore the molecular mechanisms of chemoresistance in TNBC. Firstly, we found that the mRNA expression of Notch1 and CD73 in cisplatin-treated patient material associated with poor clinical outcome. Further, both were upregulated at the protein level in cisplatin-resistant TNBC cell lines. Overexpression of Notch1 intracellular domain (termed N1ICD) increased expression of CD73, whereas knockdown of Notch1 decreased CD73 expression. Using chromatin immunoprecipitation and Dual-Luciferase assay it was identified that N1ICD directly bound the CD73 promoter and activated transcription. Taken together, these findings suggest CD73 as a direct downstream target of Notch1, providing an additional layer to the mechanisms underlying Notch1-mediated cisplatin resistance in TNBC.</p

Accurate targeting in robot-assisted TCM pulse diagnosis using adaptive sensor fusion

Accurate targeting plays an important role in the study of human-robot interaction under dynamic environments. Especially for robot-assisted Traditional Chinese Medicine (TCM) pulse diagnosis, the localization and accuracy of diagnose positions at wrist needs to be addressed. In this work, imaging photoplethysmography (iPPG) which measures the physiological changes of blood flow in artery is used as an extra modal information in addition to computer vision at localization, to alleviate the effect of approaching distance varying during the robot arm movement. Both computer vision and iPPG are fed into an adaptive fusion expert of convolutional neural networks (CNN) architecture, and this boosts the accuracy at targeting of TCM radial artery at wrist. A coherence weight of their contributions was calculated and reflected the adaptation of the CNN to distance varying