Edited by: Srikantan S. Nagarajan, University of California, San Francisco, USA
Reviewed by: Hasan Ayaz, Drexel University, USA; Barak A. Pearlmutter, National University of Ireland Maynooth, Ireland; Laurens Ruben Krol, Technische Universität Berlin, Germany
*Correspondence: Alexander von Lühmann
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Brain-Computer Interfaces (BCIs) and neuroergonomics research have high requirements regarding robustness and mobility. Additionally, fast applicability and customization are desired. Functional Near-Infrared Spectroscopy (fNIRS) is an increasingly established technology with a potential to satisfy these conditions. EEG acquisition technology, currently one of the main modalities used for mobile brain activity assessment, is widely spread and open for access and thus easily customizable. fNIRS technology on the other hand has either to be bought as a predefined commercial solution or developed from scratch using published literature. To help reducing time and effort of future custom designs for research purposes, we present our approach toward an open source multichannel stand-alone fNIRS instrument for mobile NIRS-based neuroimaging, neuroergonomics and BCI/BMI applications. The instrument is low-cost, miniaturized, wireless and modular and openly documented on
Functional Near-Infrared Spectroscopy (fNIRS) is an increasingly established technology pioneered by Jöbsis (
While first generation instruments were rather bulky and expensive, using Laser Diodes with Photo Multiplier Tubes (PMTs) (Cope and Delpy,
An increasing number of approaches using fNIRS in the field of mobile brain imaging (e.g., Piper et al.,
While the recent trend of new system designs for portability and mobility can also increasingly be observed in commercial devices, researchers will always need custom solutions for innovative approaches. To help reducing the time and effort in these cases, we present the design and a first evaluation of a configurable, miniaturized, modular, fully mobile (wireless) multichannel fNIRS system that is provided open source on
We identified aspects that are crucial to be fulfilled for a fNIRS device in the context of mobile BCI and neuroergonomics. Besides the criterion for the hardware to be comparatively low cost, these can be assigned to four groups:
The following subsections will provide detailed information on our approach to fulfill these requirements on a concept, hardware and software level.
The system concept of the modular open instrument is shown in Figure
The fNIRS modules were designed considering the current understanding of fNIRS instrumentation technology as reviewed by Scholkmann et al. (
Each module provides four dual wavelength fNIRS channels using 750 and 850 nm multi-wavelength
The LED current is regulated by adjustable current regulator circuits based on high precision amplifiers (
During lock-in demodulation, the signal is filtered by a 3rd-order Butterworth low-pass and is then again amplified (
The system is designed for Time-Division Multiplexing (TDM) of the fNIRS channels. This is a trade-off between minimizing inter-channel crosstalk, heating (Bozkurt and Onaral,
Configurable PGA gain (
As the objective of this work was the design of miniaturized fNIRS modules for mobile applications, a microcontroller (
The mainboard is a placeholder for any (custom) peripheral acquisition and control hardware. With DAQ-devices providing digital I/Os and an external power supply, any number of modules (limited by the desired dwell time and sampling rate) can be used and controlled by control- and acquisition routines written and customized by the user.
To keep the current through the LED semiconductor junctions constant and independent from variations in supply voltage and temperature, and at the same time allow intensity adjustment and current modulation for the lock-in amplification process, a customized current regulator circuit was designed (see Figure
Similar to a solution proposed by Chenier and Sawan (
As the regulator is modulated in the kHz-range, over- and undershoots influence the ideally square-wave shape of the current. To optimize the shape, a passive negative RC feedback was added and evaluated for best performance.
Shot noise is based on the quantum nature of the photons and therefore unavoidable and, for detectors without internal amplification, proportional to the square root of the average incident intensity (Scholkmann et al.,
To reduce thermal noise influences, a Si photo diode with integrated trans-impedance amplifier circuitry (OPT101) was selected for detection. Lock-in extraction of the detected signal further reduces stray light, dark current and 1/f noise influences. Placing the PGA between the detection and lock-in extraction unit enables maximum pre-amplification of the signal while amplifier noise components added in the amplification process are reduced by the subsequent lock-in demodulation. Non-physiological high frequency components of the signal are attenuated by the 3rd order low pass filter of the lock-in demodulation unit.
Figure
In the fNIRS instrument's mechanical design, the idea of modularity/scalability and robust fixation is continued by providing independent custom 3D printed solutions for the single fNIRS modules and the mainboard:
The Mainboard, Bluetooth module and batteries are worn on the upper arm of a subject in a chained multiple-unit housing (see also
For the single fNIRS modules, a new mechanical spring-loaded design was approached to optimize signal quality, sensitivity and light penetration depth together with easy and robust, adaptive fixation of the optodes (see Figure
To minimize stray light influences and for cushioning purposes, the detector and emitters are encased by an opaque cell rubber tubing. To fixate a single module to the head, a flexible ribbon with hook-and-loop fastener can be used that is sewed to the module housing.
The mechanical concept was designed to allow the modules to be used on the forehead as well as over haired regions of the head: The single spring-loaded optodes are easily accessible due to their modular fixation without a cap or other concealing elements. This enables the user to manually brush aside obstructing hair from under the optodes for better optical contact. Even though we successfully conducted measurements over hairy regions of the head, it has to be pointed out that the usability of the modules on other regions than the forehead has not been proven under controlled conditions so far.
To enable a differentiated characterization of the instrument's hardware according to functional units, evaluation and analysis was split into emitter branch (current regulation and modulation), receiver branch (lock-in module), power supply stability and overall drift characteristics:
For an estimation of the receiver sensitivity using the noise equivalent power (NEP), dark voltage noise levels (no incident light to the photo detector) were measured at the output of the lock-in-module.
The overall system drift of a single fNIRS module was specified with 20 min continuous acquisition windows of a single active channel at maximum intensity (100 mA) with the PGA set to
Simple qualitative experiments were conducted using a channel at 10–20 point Fp1 to verify significant strength of physiological information in the raw signal and its power spectrum. Amongst others, visibility and strength of pulse artifacts are indicators for the signal quality and have been widely documented in fNIRS literature with the pulse artifact's amplitude being in the order of metabolic variations due to brain activity (Boas et al.,
For verification and quantification of the device's capability to measure metabolic brain activity, a mental arithmetic BCI experiment was conducted with 12 subjects. In this experiment, it is shown that the measured hemodynamic responses can be classified on a single-trial basis, i.e., each trial can be classified as containing mental arithmetic or relaxation, instead of measuring only the difference in the average hemodynamic response.
Mental arithmetic tasks are known to illicit strong hemodynamic reactions in frontal brain areas and have been investigated in a variate of studies with fNIRS (Ang et al.,
The open fNIRS device was placed on the forehead and fixated around the head with the flexible ribbon with hook-and-loop fastener sewed to its housing. It was placed such that both active emitters were placed on the locations Fp1 and Fp2 of the international 10–20-system. The light detector was placed on AFz resulting in an emitter-detector distance of approximately 3.5 cm.
All subjects were informed prior to the experiment and gave written consent.
The signal processing of the recorded data was performed in a straight-forward and simple manner, since we focus on the developed hardware in this paper. More advanced methods have been shown to improve accuracies for classification in neuroimaging (Calhoun et al.,
After preprocessing, trials were extracted based on the experiment timings. For the pause blocks, we extracted the last 10 s of the 25–30 s pause intervals, to ensure that hemoglobin levels have returned to baseline. For each mental arithmetic trial, we extracted 10 s of data starting 5 s after stimulus presentation, to ensure that the hemodynamic response has already developed. Labels were assigned to the trials referring to either mental arithmetics or pause data. For each trial, we extracted the slope of a straight line fitted to the HbO and HbR data of each channel as a feature. The line was fitted using linear regression with a least-squares approach. Slope features have been shown to work well in previous studies (Herff et al.,
Evaluation was performed using a 10-fold cross-validation and classification by Linear Discriminant Analysis. In addition to the single trial analysis, the average hemodynamic response is calculated by averaging over all mental arithmetics or all pause trials.
The developed open modular multichannel fNIRS system (see Figure
The final instrument is characterized by:
Modularity, customization and stand-alone functionality.
Optimized adjustable current regulation and modulation with negative decoupling.
A lock-in-based signal extraction module with programmable amplification.
A 4-channel spring-loaded mechanical concept for fNIRS probe attachment to improve user comfort and robustness against movement artifacts.
A microcontroller and Bluetooth based mainboard as interchangeable peripheral control, acquisition and transmission hardware with a Bluetooth range of max. 20 m (optimal open field conditions).
Low cost components were used for the design. The total cost of the instrument's hardware for one 4 channel fNIRS module and one mainboard mainly depends on PCB fabrication costs and is approximately 200
A full documentation including detailed descriptions, schematics, and evaluation can be found in the supplementary materials for this article/on the web:
Mostly due to its higher slew rate, the AD824A showed a much faster current regulation and lower transient oscillations than the LMC6064. The experimental results for the minimization of oscillation and settling times with different decoupling capacitor values
The signal drift of a continuously active channel in Volts per second was calculated using linear least squares regression on the acquired 20 min. raw signals, yielding a negative drift coefficient of
For an approximation of the total effective phase shift between reference and demodulator input signal in the lock-in unit, the delays between both signals were measured at the 50% levels of both respective rising (
For the evaluation of the detector's sensitivity and dynamic range, the mean dark voltage signal μ
The optical powers radiated by the LED at medium intensity (
Saturation of the detection branch occurs, when the upper input voltage limit of the ADC, here 2.5 Vpp, is reached for the lowest PGA gain setting of
OPT101 Dark noise voltage | 300 μVrms @0.1–20 kHz |
OPT101 Responsivity | |
Noise equivalent power | |
Power to tissue (at peak wavelength) | |
@ |
|
@ |
|
Est. signal to noise distance | |
@ |
≈28 dB |
Eff. dynamic range | >55 dB |
Signal drift | < −1·10−6 V/s/ < −0.5% |
Sampling rate | Variable, dwell time dependent |
The ±5 V DC supply voltage drift measurements showed a stable supply voltage of +4.959 V and −4.960 V with less than 500 μV total drift in 20-min measurement periods. Evaluation of the maximum impact of the current modulation on the detecting components via the power supply revealed that current modulation flanks can create a ±2 mV high-frequency(kHz) noise around the photo detector output baseline signal that is further amplified by the PGA to strong ±100 mV peaks (at
Qualitative physiological experiments showed very clear signals and proved the basic functionality of the instrument. Figure
The average hemodynamic response (see Figure
Discrimination between pause and mental arithmetics yielded an average of 65.14% accuracy. Of the 12 recorded participants, 9 yielded accuracies significantly higher than chance level (one-sided
In the beginning of this paper, we identified system requirements for mobile fNIRS based neuroergonomics/BCI applications. The results indicate, that the presented open source device satisfies the requirements.
In the course of the experiments, both, experimentators and subjects, appraised the usability of the device to be high. Miniaturization of the modules and mobility through Bluetooth based wireless transmission allowed free movement, the use of commercial reference systems usually required longer preparation times for optode fixation and was often uncomfortable and static because of the weight of the optical fiber guides and the lack of cushioning of the optodes. In contrast, the new wearable system could be applied within several seconds and was generally perceived less cumbersome during the experiments.
The hardware evaluation results and physiological verification of the designed miniaturized fNIRS instrument indicated a sufficient signal quality and system performance for brain activity measurements with an approximated signal to noise distance of 28 dB. The lock-in amplifier, detector sensitivity, current modulation precision and drift evaluation of the device showed satisfying results comparable to other documented fNIRS devices. The physiological measurements showed the expected hemodynamic responses, classification accuracies in single-trial analysis exceeded chance level for 9 out of 12 participants and yielded results comparable to those measured with a commercial device in a similar study (Herff et al.,
Battery supply and wireless communication, low heating due to time multiplexing of the channels and the use of LEDs as light sources assured a safe usage of the device.
The scalable modular concept, configurable light intensities and detector amplification gains and the flexible parallel interface of the fNIRS modules allow easy customization and configuration of the hardware.
However, there are still several elements in the design that can be optimized to further improve instrument performance in the future.
An obvious but crucial component for the use of the fNIRS module is the data acquisition unit. When using custom hardware for data acquisition, the design and selection of the analog-to-digital converter (ADC) determine not only quantization depth but also the frequency resolution of the time division multiplexed fNIRS channels, as the ADC sampling rate has to be shared by the up to 4 active channels of one module. The ADC (LTC2486) first used on the mainboard offered 16 Bit conversion depth and exceptional DC accuracy but significantly limited time resolution due to a conversion time of type 80.3 ms. Additional experiments indicated that, using ADCs with significantly higher sample rate but lower resolution, down to 10 Bit quantization depth can suffice for reliable brain activation measurements. Future designs of the mainboard/DAQ hardware should therefore aim to use a better suited (faster) ADC to prevent the sampling frequency bottleneck. Here, the modular concept is advantageous, as the DAQ-unit can be customized and optimized independent from the hardware of the fNIRS modules.
Power supply and current modulation impact evaluation showed, that even though the implemented linear-voltage-regulator-based symmetric supply appeared to be sufficient, several improvements can be suggested for use with the fNIRS module:
To minimize crosstalk between the modulated NIR-LED current and the regulated ±5 V supply voltage rail for the detection hardware, supplying the LEDs with a separate additional voltage regulator circuit is preferable over the use of a common regulator or direct battery connection in the design. Implementation of additional high-frequency filters and enhanced stabilization are also recommended in future approaches to reduce noise pickup from external sources and further minimize LED current modulation influences on the rest of the system. The use of voltage regulators with higher efficiency can further enhance battery life and decrease heating effects, which also can—dependent on the supplying and acquisition hardware's design and layout—influence system drifts.
The phase delay dependent attenuation of approximately 0.875 by the lock-in detector is acceptable as it does not significantly decrease overall system accuracy. However, it can be further minimized: To improve the lock-in performance, an analog adjustment of the PWM reference phase could be implemented for overall phase shift compensation. Alternatively, a potentially superior approach for a next-generation design would be digital lock-in demodulation based on a microcontroller/DSP. This bears several advantages: reduced cost of hardware components, reduced power consumption and an adjustable phase shift correction and thus higher precision.
The four channel set up per module using four LEDs and one photodiode was necessary for this first approach using a single-channel analog lock-in receiver branch for a simple interface in favor of modularity. However, to further reduce energy consumption and increase channel density, future approaches should utilize configurations with more PDs measuring simultaneously. Additionally, although the fNIRS module is already compact and provides stand-alone functionality, further miniaturization is possible. A next step will be the development of entirely stand-alone modules to redundantize peripheral hardware such as the mainboard. Integrating the above mentioned insights and data acquisition, digital lock in, power management and wireless transmission components onto a further miniaturized multichannel fNIRS module could enable even more applications in and out of the lab.
The instrument can be improved and evaluated in several more ways. However, providing this fNIRS device open source, we hope that aspects of this work will be helpful to further simplify and reduce time and effort in future custom fNIRS based mobile BCI and neuroergonomics approaches.
The Review Editor Laurens Ruben Krol declares that, despite being affiliated with the same institution as the Author Alexander Von Lühmann, the review process was handled objectively. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
We acknowledge support by Deutsche Forschungsgemeinschaft and Open Access Publishing Fund of Karlsruhe Institute of Technology.