Mr. Usman Ghafoor Profile

Usman Ghafoor

at Pusan National Univ

SPIE Involvement:

Author

Publications (2)

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 | 21 February 2020 Paper

Wireless high definition neuroimaging system for fNIRS using a single photosensor

M. A. Yaqub, U. Ghafoor, Seong-Woo Woo, K.-S. Hong

Proceedings Volume 11226, 112261Q (2020) https://doi.org/10.1117/12.2544729

KEYWORDS: Photodiodes, Light emitting diodes, Brain, Neuroimaging, Microcontrollers, Brain mapping, Data acquisition, Tissues, Chromophores, Interfaces, Software development

Read Abstract +

We present a high spatiotemporal resolution system for neuroimaging using functional near-infrared spectroscopy (fNIRS). The system is configured as bundled optodes with a single photodiode (PD) and 128 dual-wavelength LEDs in a module. This system is developed using a modular approach where a single module can cover approximately 7 cm × 7 cm, while multiple modules can be used to a broader area. The system has the capacity to measure concentration changes of oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) at different brain depths ranging from 2 cm to 3.5 cm. The system also provides the superficial layer information by measuring the short-separation channels. The short-separation channels allow removal of noise and enhancement of signals. The drive circuit of LEDs is carefully designed to switch the light with appropriate intensity, which provides a stable reception for each channel. MOSFET based switching is implemented that allows sharp current switching for high-speed data acquisition. The system can display the acquired HbO and HbR signals as well as activation maps in real-time on a lab-developed Windows-based software. The hardware connects to the software using Wi-Fi. Phantom model with known optical properties and a human subject were used for testing the functionality and efficacy of the device. A complete 128 channel fNIRS sample was recorded in 25 ms. The phantom results showed reduced signal intensity when the channel separation was increased that provides the HbO and HbR. The activation was seen using HbO in the human subject while performing hand tapping task.

Proceedings Article | 1 March 2019 Paper

Initial-dip-based classification for fNIRS-BCI

A. Zafar, U. Ghafoor, M. A. Yaqub, K.-S. Hong

Proceedings Volume 10865, 108651N (2019) https://doi.org/10.1117/12.2511595

KEYWORDS: Brain-machine interfaces, Brain, Hemodynamics, Electrodes, Near infrared spectroscopy, Interfaces, Spatial resolution, Feature extraction, Brain imaging

Read Abstract +

In this paper, the effect of various channel selection strategies on the initial dip phase of the hemodynamic response (HR) using functional near-infrared spectroscopy (fNIRS) is investigated. The strategies using channel averaging, channel averaging over a local region, t-value-based channel selection, baseline correction, and vector phase analysis are examined. For t-value-based channel selection, three gamma functions are used to model the initial dip, the main HR, and the undershoot in generating the designed HR function. The linear discriminant analysis based classification accuracy is used as performance evaluation criteria. fNIRS signals are obtained from the left motor cortex during righthand thumb and little finger tapping tasks. In classifying two finger tapping tasks, signal mean and minimum value during 0~2.5 sec, as features of initial dip, are used. The results show that the active channel selected using t-value and vector phase analysis yielded the highest averaged classification accuracy. It is also found that the initial dip in the HR disappears in case of averaging overall channels. The results demonstrated the importance of the channel selection in improving the classification accuracy for fNIRS-based brain-computer interface applications. Furthermore, the use of three gamma functions can also be useful for fNIRS brain imaging for detecting the initial dip in the HR.

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