Cristin-prosjekt-ID: 2533992
Sist endret: 25. august 2022, 10:37

Cristin-prosjekt-ID: 2533992
Sist endret: 25. august 2022, 10:37
Prosjekt

brain activity monitrong and evaluation during different balancing conditions using hybrid EEG-fNIRS signals

prosjektleder

Peyman Mirtaheri
ved Institutt for maskin, elektronikk og kjemi ved OsloMet - storbyuniversitetet

prosjekteier / koordinerende forskningsansvarlig enhet

  • Institutt for maskin, elektronikk og kjemi ved OsloMet - storbyuniversitetet

Klassifisering

Vitenskapsdisipliner

Medisinsk teknologi

Emneord

Bevegelse og hjernaktivitet • EEG-fNIRS

HRCS-forskningsaktivitet

  • 1.4 Metodologi og målinger

Kategorier

Prosjektkategori

  • Doktorgradsprosjekt

Tidsramme

Avsluttet
Start: 1. februar 2020 Slutt: 31. januar 2023

Beskrivelse Beskrivelse

Tittel

brain activity monitrong and evaluation during different balancing conditions using hybrid EEG-fNIRS signals

Vitenskapelig sammendrag

Cognitive processes are involved during walking and balancing. Neurological disorders are prevalent causes of gait and balancing issues. The most common symptoms of gait and balancing problems include difficulty in walking and unsteadiness. Different studies are conducted to record underlying cognitive processes during walking and balancing using different modalities. Recording, understanding and interpreting bio-signals is one of the important research area. To record brain activity, several invasive and non-invasive modalities are used such as EEG, ECoG, MRI and fNIRS. Among many non-invasive brain signal acquisition technologies, fNIRS has advantages of high spatial resolution, low cost, portability and usage in long-duration experiments. Which really helps in real-life monitoring. Similarly, EEG is also very widely used modality due to its ad- vantage of being non-invasive, high temporal resolution and portable. Hybridization of bio-signals helps to increase number of control commands, enhance accuracy and reduce signal detection time. Studies show that hybrid EEG-fNIRS signals helps in getting better classification, enhanced accuracy and increased number of control commands. In recent years, advance machine learning (ML) techniques are helping to give better understanding of these signals. The connection between the human walking and body balancing has not been extensively researched. Study and analysis of the neurological causes of gait and balance are among the promising research issues. Not only the neural correlates of different gait or balance problems need to be investigated but also the effects on brain signals due to age, disease and multi-tasking during walking needs to be seen. Selection of significant features from these bio-signals and their classification can give better understanding and Appendix - 22interpretation of causes that effects gait or balancing. In this project, brain signal acquired from hybrid EEG-fNIRS modalities will be studied using modern ML algorithm to understand and find the relation between human motion/balance and cognitive processes. In this project collaborative research will be carried out between TKD OsloMet, Oslo University hospital and a Norwegian company "Gaitline AS". The result of this research will provide an insight into possible neurological causes of difficulties in balancing/walking and its solution.

Utstyr

Hybrid or individual use of fNIRS ( functional near-infrared spectroscopy) and EEG (electroencephalogram)

prosjektdeltakere

prosjektleder
Aktiv cristin-person

Peyman Mirtaheri

  • Tilknyttet:
    Prosjektleder
    ved Institutt for maskin, elektronikk og kjemi ved OsloMet - storbyuniversitetet

Anis Yazidi

  • Tilknyttet:
    Prosjektdeltaker
    ved OsloMet - storbyuniversitetet
Aktiv cristin-person

Per Kristian Eide

  • Tilknyttet:
    Prosjektdeltaker
    ved Oslo universitetssykehus HF
  • Tilknyttet:
    Prosjektdeltaker
    ved Nevrokirurgisk avdeling ved Oslo universitetssykehus HF

Haroon Khan

  • Tilknyttet:
    Prosjektdeltaker
    ved Institutt for maskin, elektronikk og kjemi ved OsloMet - storbyuniversitetet
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Resultater Resultater

Classification of Individual Finger Movements from Right Hand Using fNIRS Signals.

Khan, Haroon; Noori, Farzan Majeed; Yazidi, Anis; Uddin, Md Zia; Khan, M.N Afzal; Mirtaheri, Peyman. 2021, Sensors. PNU, SINTEF, OSLOMET, NTNU, UIO, OUS, MTUVitenskapelig artikkel

Classification of Individual Finger Movements using fNIRS for BCI Applications.

Khan, Haroon; Noori, Farzan Majeed; Naseer, Noman; Qureshi, Nauman Khalid; Nazeer, Hammad; Mirtaheri, Peyman. 2021, Society of fNIRS Virtual Conference 2021. OSLOMET, UIOPoster

Enhancing Classification Accuracy of Transhumeral Prosthesis: A Hybrid sEMG and fNIRS approach.

Yousaf Sattar, Neelum; Kausar, Zareena; Usama, Syed Ali; Naseer, Noman; Farooq, Umer; Abdullah, Ahmed; Hussain, Syed Zahid; Shahbaz Khan, Umar; Khan, Haroon; Mirtaheri, Peyman. 2021, IEEE Access. AU, OSLOMET, PAKISTANVitenskapelig artikkel

An overview of assessment tools for determination of biological Magnesium implant degradation.

Hassan, Hafiz Wajahat; Grasso, Valeria; Korostynska, Olga; Khan, Haroon; Jose, Jithin; Mirtaheri, Peyman. 2021, Medical Engineering and Physics. OSLOMET, NEDERLANDVitenskapelig oversiktsartikkel/review

Prefrontal Cortex Activation Measured during Different Footwear and Ground Conditions Using fNIRS – A Case Study.

Khan, Haroon; Nazeer, Hammad; Engell, Håvard; Naseer, Noman; Korostynska, Olga; Mirtaheri, Peyman. 2021, International Conference on Artificial Intelligence and Mechatronics Systems (AIMS 2021). NMBU, OSLOMETVitenskapelig foredrag
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