Mr. Debanjan Borthakur Profile

Debanjan Borthakur

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 5 March 2021 Poster + Presentation + Paper

A machine learning approach to classify working memory load from optical neuroimaging data

Manob Jyoti Saikia, Shiba Kuanar, Debanjan Borthakur, Maria Vinti, Thupten Tendhar

Proceedings Volume 11629, 116292K (2021) https://doi.org/10.1117/12.2578952

KEYWORDS: Neuroimaging, Machine learning, Hemodynamics, Brain, Prefrontal cortex, Near infrared spectroscopy, Interfaces, Feature extraction, Data conversion

Read Abstract +

Functional near-infrared spectroscopy (fNIRS) is a brain imaging methodology that is appertaining to imaging the hemodynamic responses or blood oxygenation level-dependency of the brain. Machine learning (ML) has the potential to classify different mental states from the fNIRS signal to build a brain-computer interface (BCI) system. In this work, we have used a portable fNIRS system, WearLight, to image the prefrontal cortex of the brain of 12 participants while performing n-back working memory (WM) tasks. We converted the time-series raw data to the hemodynamic response by implementing a processing pipeline. A simple method based on checking the hemodynamic signal level of all the channels during task blocks with respect to the rest periods was used to identify the thee most dominant channels. We extracted eight important features from the hemodynamic brain signals to construct the feature matrix and trained six different k-nearest neighbors (k-NN) ML classifiers. The performance of the six k-NN classifiers was evaluated with the new experimental data sets. The results have shown that Weighted and Fine k-NN performed best (75%) in classifying the 4 different WM loads. The method is encouraging for real-time classification of the mental workload using a portable fNIRS system augmented by advanced machine learning techniques.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years