Sample Data download → fNIR_EMG.zip
(extract biceps_EMG_fnir_demo.acq)
Using functional Near Infrared (fNIR) to study muscle function can provide greater understanding of the underlying control mechanisms that couple oxygen delivery to oxidative metabolism. Near optodes are recorded as well to help measure the impact of cutaneous tissue in the signal.
Surface electromyography (sEMG) can be included to detect motor unit action potentials (MUAP) in muscle fibers as an indication of muscle activation, force production, or fatigue.
fNIR Series imagers allow high-density muscle oxygenation measurements and can be used for a wide array of protocols, such as: regional load and regional differences; oxidative capacity; lateral imbalances; quadriceps measurement; biceps during static and dynamic load; and more.
To measure from multiple muscles, use the S-imager, which allows for up to 6 sensors to be attached, or combine multiple fNIR imagers in COBI Studio. The COBI Cognitive Optical Brain Imaging Studio software platform is designed for fNIR experimentation and supports simultaneous acquisition from multiple devices/people.
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This is one of many ADVANCED FEATURES for the selected Application. Scroll down for hardware options.Hardware
Imager: FNIR203C with Sensor: RXFNIR-2000-5 5ch sensor pad → see all fNIR Systems for Research
EMG: BN-EMG2 wireless EMG amp with transmitter
Electrodes EL504 x 2 and Lead Set BN-EL30-LEAD2
WRAP1 Self-adhesive stretch wrap
Participant
Place NIRS sensor and EMG electrodes in the direction of muscle fibers on the biceps brachii, symmetrical with the line between medial acromion and fossa cubit at 1/3 from fossa cubit.
Sensor: Place the sensor pad on the anterior side of muscle (muscle belly) and use WRAP1 to secure the placement of the sensor pad on the participant’s arm.
Electrodes: Place electrode approximately 10 cm apart along the medial side of the muscle’s short head and connect Vin+ and Vin– leads.
Experimental Procedure
Record baseline (~120 sec): Participant begins in a standing, relaxed position with arms down next to the body.
Record muscle activation (~60 sec): After baseline, instruct the participant to flex his/her biceps for about 60 seconds and return to initial relaxed position. Manually insert an event marker to indicate the onset of the flexing motion.
Alternate protocol: Record 30 sec baseline. Pull up against resistance with the biceps at 50% MVC until exhaustion. After 20 minutes, perform semi-dynamic contraction alternating for 4 sec each at 20% and 60% MVC until exhaustion.
Analysis
COBI Studio Modern was used to record the light intensity graph from the fNIRS sensor pad. The automated calculation of oxy and deoxy hemoglobin trails was performed with the use of fNIRSoft.
Figure 1: Oxy (red) and deoxy (blue) hemoglobin trail on CH1 of RXFNIR-2000-5 Sensor
Marker 1 was inserted manually to indicate the onset of the flexing motion.
The oxygenated hemoglobin data was exported from fNIRSoft as a .csv file and subsequently imported into AcqKnowledge.
Figure 2: CH1 raw EMG, CH2 imported oxy hemoglobin trail, CH3 Root Mean Square (RMS) of the EMG signal
The experiment showed an initial decrease in the oxy hemoglobin and increase in the deoxy hemoglobin concentration in the muscle tissue during the muscle flexing part of the experiment. A slow recuperation of the HbO was observed during the flexing stage. After the active part of the experiment, the HbR returned almost immediately to pre-flexing concentration levels, whereas the HbO concentration exceeded the resting period levels and gradually returned to resting state levels over time.
All concentration levels and changes indicated are relative with respect to initial baseline recorded prior to the start of the actual experiment.
In this fNIRS training, you will see a typical analysis workflow for fNIRS data in experiments featuring both event-based and continuous stimulation. Learn about a powerful yet easy-to-use software package with both a graphical user interface and scripting capability (for automation).
What You Will Learn:
• Preparing data for analysis
• Signal conditioning, noise removal, and managing artifacts
• Best practices for visualizing recorded data
• Block analysis using event markers and/or time
• Further temporal and spatial processing of data, including cell-by-cell processing
• Automation of analysis procedures
• Performing common statistical comparisons (t-test, ANOVA)
• Spatial visualization
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