Conference Paper2015Cited by 3

Decoding human brain states using Transcranial Doppler Ultrasonography

Ashraful Islam, Eshtiak Ahmed, Ashfaqul Islam, Jie Lu, Farhana Sarkar, Khondaker A Mamun

2015 International Conference on Electrical Engineering and Information Communication Technology (ICEEICT)

IEEE, pp. 1–6

  • Good Health and Well-being — Relevance: 69%
DOI: 10.1109/iceeict.2015.7307397URL

Abstract

Transcranial Doppler Ultrasonography (TCD) has emerged as a Brain-computer Interface (BCI) modality with great promise and potential. TCD is a non-invasive ultrasound technology that detects the changes in cerebral blood flow velocity (CBFV) which has recently been used as a functional brain imaging tool to examine the effects of mental tasks on the blood flow velocities, particularly focusing on the middle cerebral arteries (MCAs). Individuals, having cognitive awareness but severe neuro-motor disabilities like muscular dystrophy or spinal cord injuries, face difficulties to communicate with their surroundings. Changes in blood flow velocity in the MCAs are an indication of mental activation in human brain which could lead us to identifying various brain states. Blood flow lateralization elicited by mental tasks, such as verbal fluency and visualization tasks, has been detected using TCD in a number of recent studies. In this study, CBFV was measured simultaneously within the left and right middle cerebral arteries for alternated mental activation and relaxation tasks from ten able-bodied participants. Signal features were extracted and then selected using the Fisher Criterion and Sequential Forward Search (SFS) approach. Two classification methods, Linear Discriminant Analysis and a Naïve Bayes Classifier were employed to decode mental activation and relaxation tasks. Preliminary results show an average accuracy, sensitivity (activation) and specificity (relaxation) of 79.13 ± 3.85%, 78.88 ± 6.44% and 79.38 ± 5.57% respectively. The results are very much encouraging and indicate that TCD could be a viable alternative BCI modality and demands for further exploration for improvement and potential real world applications.

Keywords

  • cerebral blood flow velocity
  • brain-computer interface
  • Linear discriminant analysis
  • Transcranial Doppler
  • sequential forward search
  • middle cerebral artery
  • Naive Bayes classifier
  • mental state decoding

CCDS Authors

Ashraful Islam, PhD
Ashraful Islam

Ashraful Islam, PhD

  • Center Director, CCDS
  • Director, HCI Wing

Assistant Professor

Department of Computer Science and Engineering

Independent University, Bangladesh (IUB)

Published at

CHI, UIST, UbiComp, IJCNN, ICIP, HCII, ICDMW, BLP, IEEE Internet of Things Journal, JMIR, JMIR Human Factors, JMIR Formative Research, IEEE Access, Smart Agricultural Technology, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Human-Computer Interaction, AI for Social Good, AI for Public Health, AI for Impact

Eshtiak Ahmed
Eshtiak Ahmed

Eshtiak Ahmed

  • Collaborator, HCI Wing

Doctoral Researcher

Gamification Group

Tampere University, Finland

Published at

UbiComp, ICIP, HCII, IEEE Access

Research Interests

Human-Computer Interaction, Human-Robot Interaction, Gamification and Assistive Technologies

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Cited By

  1. 1.Eshtiak Ahmed, Ashraful Islam, Farhana Sarker, Mohammad Nurul Huda, Khondaker Abdullah-Al-Mamun. (2016). A road to independent living with smart homes for people with disabilities. , 15, 472–477[10.1109/iciev.2016.7760048]
  2. 2.Rosita Rabbito, Leonardo Ermini, Caterina Guiot, Silvestro Roatta. (2025). Transcranial Doppler-Based Neurofeedback to Improve Hemispheric Lateralization. Applied Sciences, 15(10), 5763[10.3390/app15105763]
  3. 3.Yutong Zhang. (2024). Innovative Non-Invasive Brain-Computer Interfaces for Addressing Cognitive Decline in the Elderly. Applied and Computational Engineering, 106(1), 156–160[10.54254/2755-2721/106/20241114]
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