This paper presents a novel two-axis shape sensor based on optical optoelectronic sensors and integrated with a flexible manipulator arm to measure the overall shape of the robotic arm. The disc-shape bio-compatible sensor presented here can be embedded as a sensing system into flexible manipulators and is applicable to the geometry of its structure and to the structure of any other similar multi-segment robotic manipulator. Design and calibration procedures of the device are introduced: experimental results allow defining a sensor matrix for real-time estimation of the pitch and roll of the plate above the sensor and confirms the usefulness of the proposed optical sensing approach. Position on Calibration Base (Fig. 9) Pitch and Roll Components
Quantitative electroencephalography (QEEG) analysis is commonly adopted for the investigation of various neurological disorders, revealing electroencephalogram (EEG) features associated with specific dysfunctions. Conventionally, topographies are widely utilized for spatial representation of EEG characteristics at specific frequencies or frequency bands. However, multiple topographies at various frequency bands are required for a complete description of brain activity. In consequence, use of topographies for the training of deep learning algorithms is often challenging. The present study describes the development and application of a novel QEEG feature image that integrates all required spatial and spectral information within a single image, overcoming conventional obstacles. EEG powers recorded at 19 channels defined by the international 10–20 system were pre-processed using the EEG auto-analysis system iSyncBrain®, removing the artifact components selected through independent component analysis (ICA) and rejecting bad epochs. Hereafter, spectral powers computed through fast Fourier transform (FFT) were standardized into Z-scores through iMediSync, Inc.’s age- and sex-specific normative database. The standardized spectral powers for each channel were subsequently rearranged and concatenated into a rectangular feature matrix, in accordance with their spatial location on the scalp surface. Application of various feature engineering techniques on the established feature matrix yielded multiple types of feature images. Such feature images were utilized in the deep learning classification of Alzheimer’s disease dementia (ADD) and non-Alzheimer’s disease dementia (NADD) data, in order to validate the use of our novel feature images. The resulting classification accuracy was 97.4%. The Classification criteria were further inferred through an explainable artificial intelligence (XAI) algorithm, which complied with the conventionally known EEG characteristics of AD. Such outstanding classification performance bolsters the potential of our novel QEEG feature images in broadening QEEG utility.
Introduction: Alzheimer's disease dementia (ADD) has now become a crucial concern for modern society as a result of increased life expectancy. However, it is often difficult for a majority of the population to afford expensive medical imaging tests for accurate diagnosis. As a solution, quantitative analysis of electroencephalography (EEG) that aids in a sufficient description of brain activities can be employed as a cost-effective, safe and objective diagnostic tool. In the presented research, we employed diverse QEEG features at both channel- and source-level to enhance the robustness of our previously established artificial intelligence (AI) model that distinguishes non-ADD (NADD) data from ADD data.Method 594 NADD and 137 ADD subjects’ EEG data were employed for the presented research. artifact-free data were obtained through the application of independent component analysis (ICA) and bad epoch rejection. Absolute and relative power spectra at 19 channels were first computed, followed by the estimation of source-level power spectra through standardized low-resolution brain electromagnetic tomography (s-LORETA). Through further feature engineering, functional brain networks were also obtained. The established channel-level features were transformed into images that spatially allocate absolute and relative spectral powers, which were utilized for the training of deep neural network structures. Moreover, source-level spectral powers and functional brain networks were adopted for the training of a tree-based machine learning algorithm. Prediction probabilities of the established classification models were ensembled through the voting method and returned the final classification result.Results The best classification accuracies of the absolute and relative channel-level spectral power image-based deep neural network models were 85.3% and 86.5% respectively. The tree-based model that has been trained with source-level features resulted in an accuracy of 87.7%. The accuracy of the ensemble model was 88.5%, which demonstrates the compensatory interaction among the models.Conclusions The promising classification results indicate the potential behind EEG-AI models for the analysis of neurodegenerative disorders. Through continuous analysis of several independent QEEG features of varying aspects, we may soon be able to more aptly diagnose several neurological disorders.
BackgroundAlzheimer’s disease is the most common cause of dementia, which destroys nerve cells in the brain through the formation and accumulation of amyloid plaques. However, various other pathologies of dementia also exist, such as Lewy body pathology where the destruction is caused by the accumulation of Lewy body peptides. Differing dementia pathologies carry distinguishing symptoms and treatment methods; hence we cannot disregard the importance of correct identification of the pathology.Currently, the correct pathology can be identified through positron emission tomography (PET), which is expensive and results in exposure to harmful ionizing radiation. Therefore, the present study aims to overcome the disadvantages of current screening methods and distinguish the quantitative electroencephalography (QEEG) of two dementia pathologies through machine learning.MethodEEG data employed in the present study were recorded at the 19 electrode locations defined by the international 10‐20 system, in eyes‐closed resting‐state condition. The acquired data were then re‐referenced to a common average reference. Hereafter, bandpass filtering at 0.1‐45.5Hz, bad epoch rejection and independent component analysis (ICA) was performed to remove noise components from the data. Standardized low resolution brain electromagnetic tomography (sLORETA) was further used for mathematical estimation of source cortical activity, yielding data for 68 cortical regions based on the Desikan‐Killiany atlas.Gamma wave (30‐45Hz) features were excluded from the data due to is vulnerability to electromyography. Further feature reduction was also performed, through the designated criteria on p‐value threshold (0.05) and feature importance threshold determined by shapely values. The final dataset (N = 104; 30 ADD, 74 DLB) was established, where 20% of the data (N =21; 6 ADD, 15 DLB) were randomly selected and excluded as test data for model verification.ResultThe final machine learning model trained and tested through the dataset yielded test accuracy at 85.7% with ADD sensitivity at 83.3%, DLB sensitivity at 86.7%. The 5 fold cross validation accuracy was at 82.1%.ConclusionThe model developed in the present study yielded a promising classification performance, through EEG data which is cheap and harmless to record. Such QEEG‐based classification models may sufficiently replace the PET in future, resolving current disadvantages.
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