Fixed-Value MBLL based Cognitive Hemodynamic response assessment using P-fNIRS system: Applications to Deep Learning Brain Machine Interface (BMI) / (Record no. 610627)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03519nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 670 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Asgher,Umer |
| 245 ## - TITLE STATEMENT | |
| Title | Fixed-Value MBLL based Cognitive Hemodynamic response assessment using P-fNIRS system: Applications to Deep Learning Brain Machine Interface (BMI) / |
| Statement of responsibility, etc. | Umer Asgher |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Islamabad : |
| Name of producer, publisher, distributor, manufacturer | SMME- NUST; |
| Date of production, publication, distribution, manufacture, or copyright notice | 2020. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 172p. |
| Other physical details | Soft Copy |
| Dimensions | 30cm |
| 500 ## - GENERAL NOTE | |
| General note | Humans in the modern systems not only interact with other humans but also have to<br/>interact with intelligent machines, robots in form of cyber physical systems to collaborate in<br/>order to carry out different tasks in real working environment. The modern Industrial system<br/>comprises of humans, machines, and cyber systems with a collective aim of optimized<br/>industrial manufacturing objectives, human factors, and ergonomics goals. Different macrohuman factors are considered while designing and formulating human work safety of such<br/>systems and one of the important neuroergonomic factors in is the Cognitive and Mental<br/>Workload (C-MWL). The mental workload (MWL) in the human’s brain is measured with<br/>difference non-invasive neuroimaging techniques. Most of the cognitive load measuring<br/>methods either require massive system protocols like fMRI (functional magnetic resonance<br/>imagining), positron-emission tomography (PET) or strict human anatomical movements<br/>restrictions like electroencephalogram (EEG) and magnetoencephalography (MEG). To<br/>address these limitations, fNIRS (functional Near infrared Spectroscopy) is used in this<br/>research to measure the hemodynamic changes in the human brain’s tissues as a measure of<br/>the brain activity.<br/>The brain’s hemodynamic signals are measured using a light weight portable fNIRS<br/>system (P-fNIRSSyst) that is designed to measure relative change in concentration of<br/>chromophores (oxy and deoxy hemoglobin) in brain tissues. In this study a novel variant of<br/>MBLL (Modified Beer-lambert Law) is designed by keeping the previous intensity value as a<br/>reference by taking the average from initial four seconds activity stimuli in optical density. The<br/>four second stimuli average in novel and important in calculation the changes in concentration<br/>of chromophores. This novel variant of MBLL is Fixed Value - Modified Beer-lambert Law<br/>(FV-MBLL). In this research, MWL is measured and classified in different real time working<br/>environments. The two-state cognitive load is measured with fNIRS system and classified<br/>using FV-MBLL using machine learning techniques like k-nearest neighbors (k-NN), support<br/>vector machines (SVM), and artificial neural networks (ANN). The classification accuracies<br/>of FV-MBLL are better than MBLL. The research further explores the classification<br/>capabilities of deep neural networks (DNN) such as convolutional neural network s (CNN) and<br/>Long short-term memory (LSTM) for the first time in assessment and classification of four-<br/>iv<br/>state MWL. The classification accuracies of LSTM outperform not only ML algorithms (SVM,<br/>KNN and ANN) but CNN as well in classification of multi-state MWL. The research<br/>experimental validation is performed using the accuracies with MWL that are further utilized<br/>in neurorehabilitation as neurofeedback to operate bionic systems using Brain Machine<br/>Interface (BMI). |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | PhD in Design and Manufacturing Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor : Prof. Dr. Riaz Ahmed Mufti |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://10.250.8.41:8080/xmlui/handle/123456789/13196">http://10.250.8.41:8080/xmlui/handle/123456789/13196</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Thesis |
| Withdrawn status | Permanent Location | Current Location | Shelving location | Date acquired | Full call number | Barcode | Koha item type |
|---|---|---|---|---|---|---|---|
| School of Mechanical & Manufacturing Engineering (SMME) | School of Mechanical & Manufacturing Engineering (SMME) | E-Books | 07/29/2024 | 670 | SMME-Phd-9 | Thesis |