scholarly journals An artificial neural-network approach to identify motor hotspot for upper-limb based on electroencephalography: a proof-of-concept study

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Ga-Young Choi ◽  
Chang-Hee Han ◽  
Hyung-Tak Lee ◽  
Nam-Jong Paik ◽  
Won-Seok Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Motor Cortex ◽  
Transcranial Electrical Stimulation ◽  
Proof Of Concept ◽  
Mean Error ◽  
Eeg Data ◽  
Identification Approach ◽  
Artificial Neural ◽  
Error Distance

Abstract Background To apply transcranial electrical stimulation (tES) to the motor cortex, motor hotspots are generally identified using motor evoked potentials by transcranial magnetic stimulation (TMS). The objective of this study is to validate the feasibility of a novel electroencephalography (EEG)-based motor-hotspot-identification approach using a machine learning technique as a potential alternative to TMS. Methods EEG data were measured using 63 channels from thirty subjects as they performed a simple finger tapping task. Power spectral densities of the EEG data were extracted from six frequency bands (delta, theta, alpha, beta, gamma, and full) and were independently used to train and test an artificial neural network for motor hotspot identification. The 3D coordinate information of individual motor hotspots identified by TMS were quantitatively compared with those estimated by our EEG-based motor-hotspot-identification approach to assess its feasibility. Results The minimum mean error distance between the motor hotspot locations identified by TMS and our proposed motor-hotspot-identification approach was 0.22 ± 0.03 cm, demonstrating the proof-of-concept of our proposed EEG-based approach. A mean error distance of 1.32 ± 0.15 cm was measured when using only nine channels attached to the middle of the motor cortex, showing the possibility of practically using the proposed motor-hotspot-identification approach based on a relatively small number of EEG channels. Conclusion We demonstrated the feasibility of our novel EEG-based motor-hotspot-identification method. It is expected that our approach can be used as an alternative to TMS for motor hotspot identification. In particular, its usability would significantly increase when using a recently developed portable tES device integrated with an EEG device.

scholarly journals An artificial Neural-Network Approach for Motor Hotspot Identification Based on Electroencephalography: A Proof-of-Concept Study

2021 ◽  
Author(s):  
Ga-Young Choi ◽  
Chang-Hee Han ◽  
Hyung-Tak Lee ◽  
Nam-Jong Paik ◽  
Won-Seok Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Motor Cortex ◽  
Transcranial Electrical Stimulation ◽  
Proof Of Concept ◽  
Mean Error ◽  
Eeg Data ◽  
Identification Approach ◽  
Artificial Neural ◽  
Error Distance

Background: To apply transcranial electrical stimulation (tES) to the motor cortex, motor hotspots are generally identified using motor evoked potentials by transcranial magnetic stimulation (TMS). The objective of this study is to validate the feasibility of a novel electroencephalography (EEG)-based motor-hotspot-identification approach using a machine learning technique as a potential alternative to TMS. Methods: EEG data were measured using 63 channels from thirty subjects as they performed a simple finger tapping task. Power spectral densities of the EEG data were extracted from six frequency bands (delta, theta, alpha, beta, gamma, and full) and were independently used to train and test an artificial neural network for motor hotspot identification. The 3D coordinate information of individual motor hotspots identified by TMS were quantitatively compared with those estimated by our EEG-based motor-hotspot-identification approach to assess its feasibility. Results: The minimum mean error distance between the motor hotspot locations identified by TMS and our proposed motor-hotspot-identification approach was 0.22 & 0.03 cm, demonstrating the proof-of-concept of our proposed EEG-based approach. A mean error distance of 1.32 & 0.15 cm was measured when using only nine channels attached to the middle of the motor cortex, showing the possibility of practically using the proposed motor-hotspot-identification approach based on a relatively small number of EEG channels. Conclusion: We demonstrated the feasibility of our novel EEG-based motor-hotspot-identification method. It is expected that our approach can be used as an alternative to TMS for motor hotspot identification. In particular, its usability would significantly increase when using a recently developed portable tES device integrated with an EEG device.

scholarly journals Pengaruh Latar Belakang Warna pada Objek Gambar terhadap Hasil Ekstraksi Sinyal EEG

2017 ◽  
Vol 7 (2) ◽  
pp. 161 ◽  
Author(s):  
Catur Atmaji ◽  
Zandy Yudha Perwira
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Training System ◽  
Training Data ◽  
Spectrum Estimation ◽  
Quality Classification ◽  
Eeg Data ◽  
Two Systems ◽  
Artificial Neural ◽  
Color Background

In this study, observation on the differences in features quality of EEG records as a result of training on subjects has been made. The features of EEG records were extracted using two different methods, the root mean square which is acquired from the range between 0.5 and 5 seconds and the average of power spectrum estimation from the frequency range between 20 and 40Hz. All of the data consists of a 4-channel recording and produce good quality classification on artificial neural network, with each of which generates training data accuracy over 90%. However, different results are occured when the trained system is tested on other test data. The test results show that the two systems which are trained using training data with object with color background produce higher accuracy than the other two systems which are trained using training data with object without background color, 63.98% and 60.22% compared to 59.68% and 56.45% accuracy respectively. From the use of the features on the artificial neural network classification system, it can be concluded that the training system using EEG data records derived from the visualization of object with color background produces better features than the visualization of object without color background.

scholarly journals Predictive Modeling of a Leaf Conceptual Midpoint Quasi-Color (CMQ) Using an Artificial Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3938
Author(s):  
Ivan Simko
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Predictive Modeling ◽  
Leafy Vegetables ◽  
Proof Of Concept ◽  
Plant Leaves ◽  
Actual Color ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Very High

The color of plant leaves is moderated by the content of pigments, which can show considerable dorsiventral distribution. Two typical examples are leafy vegetables and ornamentals, wherein red and green color surfaces can be seen on the same leaf. The proof of concept is provided for predictive modeling of a leaf conceptual mid-point quasi-color (CMQ) from the content of pigments. The CMQ idea is based on the hypothesis that the content of pigments in leaves is associated with the combined color from both surfaces. The CMQ, which is calculated from CIELab color coordinates at adaxial and abaxial antipodes, is thus not an actual color, but a notion that can be used in modeling. The CMQ coordinates, predicted from the content of chlorophylls and anthocyanins by means of an artificial neural network (ANN), matched well with the CMQ coordinates empirically found on photosynthetically active leaves of lettuce (Lactuca sativa L.), but also with other plant species with comparable leaf attributes. Modeled values of lightness (qL*) decreased with the increasing content of both pigments, while the redness or greenness (qa*) and yellowness or blueness (qb*) of the CMQ were affected more by a relative content of chlorophylls and anthocyanins in leaves. The highest vividness of quasi-colors (qC*) was modeled for leaves with a high content of either pigment alone. The model predicted a substantially duller quasi-color for leaves with chlorophylls and anthocyanins present together, particularly when both pigments were present at very high levels.

scholarly journals One-time learning in a biologically-inspired Salience-affected Artificial Neural Network (SANN)

2019 ◽  
Author(s):  
Leendert A Remmelzwaal ◽  
George F R Ellis ◽  
Jonathan Tapson
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Neural Dynamics ◽  
Proof Of Concept ◽  
Activation Functions ◽  
Learning Networks ◽  
Biologically Inspired ◽  
Artificial Neural ◽  
Validation Testing ◽  
And Robotics

AbstractIn this paper we introduce a novel Salience Affected Artificial Neural Network (SANN) that models the way neuromodulators such as dopamine and noradrenaline affect neural dynamics in the human brain by being distributed diffusely through neocortical regions. This allows one-time learning to take place through strengthening entire patterns of activation at one go. We present a model that accepts a salience signal, and returns a reverse salience signal. We demonstrate that we can tag an image with salience with only a single training iteration, and that the same image will then produces the highest reverse salience signal during classification. We explore the effects of salience on learning via its effect on the activation functions of each node, as well as on the strength of weights in the network. We demonstrate that a salience signal improves classification accuracy of the specific image that was tagged with salience, as well as all images in the same class, while penalizing images in other classes. Results are validated using 5-fold validation testing on MNIST and Fashion MNIST datasets. This research serves as a proof of concept, and could be the first step towards introducing salience tagging into Deep Learning Networks and robotics.

scholarly journals Change Point Determination for an Attribute Process Using an Artificial Neural Network-Based Approach

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Yuehjen E. Shao ◽  
Ke-Shan Lin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Change Point ◽  
Vital Role ◽  
Statistical Process ◽  
Identification Approach ◽  
Point Determination ◽  
Mathematical Difficulties ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The change point identification has played a vital role in process improvement for an attribute process. This identification is able to effectively help process personnel to quickly determine the corresponding root causes and significantly improve the underlying process. Although many studies have focused on identifying the change point of a process, a generic identification approach has not been developed. The typical maximum likelihood estimator (MLE) approach has limitations: particularly, the known prior process distribution and mathematical difficulties. These deficiencies are commonly encountered in practice. Accordingly, this study proposes an artificial neural network (ANN) mechanism to overcome the difficulties of typical MLE approach in determining the change point of an attribute process. Specifically, the performance among the statistical process control (SPC) chart alone, the typical MLE approach, and the proposed ANN mechanism are investigated for the following cases: (1) a known attribute process distribution with the associated MLE being available to be used, (2) an unknown attribute process distribution with the MLE being unable to be used, and (3) an unknown attribute process distribution with the MLE being misused. The superior results and the performance of the proposed approach are reported and discussed.

Classification of Quantitative EEG Data by an Artificial Neural Network: A Preliminary Study

1996 ◽  
Vol 33 (2) ◽  
pp. 106-112 ◽  
Author(s):  
L.A. Riquelme ◽  
B.S. Zanuto ◽  
M.G. Murer ◽  
R.J. Lombardo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Quantitative Eeg ◽  
Eeg Data ◽  
Preliminary Study ◽  
Artificial Neural

Tracking rower motion without on-body sensors using an instrumented machine and an artificial neural network

2021 ◽  
pp. 175433712110141
Author(s):  
Karim BenSiSaid ◽  
Noureddine Ababou ◽  
Amina Ababou ◽  
Daniel Roth ◽  
Sebastian von Mammen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Angular Displacement ◽  
Human Motion ◽  
Mean Error ◽  
Displacement Sensors ◽  
Marquardt Algorithm ◽  
Flexion Extension ◽  
Body Sensors ◽  
Artificial Neural

Human motion tracking is an active field of research driven by its diverse applications in areas such as health care, daily activity recognition, sports, etc. In sports applications, tracking rowing motion is performed to meet several goals, including prevention of injuries, improvement of performance or provision of virtual coaching. Different established approaches rely on on-body sensors to capture rowing motion. While on-body sensors are effective and straight forward to implement, they can disturb the athlete and negatively impact the training. In this paper, an approach is presented to track rowing motion without body-worn sensors or cameras. Instead, sensors were attached to an indoor rowing machine that tracked the motion of its sliding seat, lever handle and the force exercised on the seat. In particular, the motion was tracked by means of linear and angular displacement sensors, as well as force sensors placed underneath the seat. The respective variables were fed into an Artificial Neural Network (ANN) to predict the coordinates of the rower’s shoulder, which in turn, were used to geometrically infer the angles of the shoulder, elbow, hip and thoracolumbar flexion-extension. A successful ANN architecture was iteratively designed by using the Levenberg-Marquardt algorithm and varying the number of hidden neurons in one hidden layer. A comparison between ANN-predicted and experimentally obtained shoulder coordinates from an optical motion capture system showed a mean error of less than 4 cm, which led to an angle mean error value as low as 2.01°. A rigged avatar was used to visually verify the reproduced motion. The avatar animation was well-received by experts, especially considering the shoulder adduction-abduction in the frontal plane.

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