Here you can find documentation and technical details for the miniaturized stand alone open Near InfraRed Spectroscopy (NIRS) hardware.

We are aware, that the provided design is far from perfect and can be further optimized in several ways.
However, with the presented version having passed several evaluation and verification steps and proving full functionality, we feel that the presented documentation and materials for this open NIRS  instrument can significantly help anybody who is designing his own NIRS hardware from scratch.
This instrument should therefore not be viewed as complete in the sense that simple rebuilding is the first choice (even though this is possible and will result in a running NIRS instrument) but as a guidance for further designs that will then be able to profit from the experience and building blocks of the hardware, software and mechanical concept presented here.

For the most compact, revised and up to date (Jan. 2015) documentation of the openNIRS system, please see and refer to the following publication:

von Lühmann, Herff, Heger and Schultz (2015), "Towards a wireless open source instrument:
functional Near-Infrared Spectroscopy in mobile neuroergonomics and BCI applications
", frontiers in Human Neuroscience, doi:10.3389/fnhum.2015.00617

For a next generation hybrid EEG-fNIRS-Accelerometer architecture that is built on top of the openNIRS technology and significantly increases hardware performance, miniaturization and functionality, please see the following new open access M3BA publication. However, if you want to use this commercially, please note that the M3BA design is patented. Feel free to contact us for questions regarding licensing options.

von Lühmann, Wabnitz, Sander and Müller (2016), "M3BA: A Mobile, Modular, Multimodal Biosignal Acquisition architecture for miniaturized EEG-NIRS based hybrid BCI and monitoring", IEEE Transactions on Biomedical Engineering, doi:10.1109/TBME.2016.2594127

Both the openNIRS and M3BA architectures are part of the following PhD thesis, where you can also find applications and experiments, and novel multimodal signal processing methods based on machine learning:

von Lühmann, Alexander (2018), "Multimodal instrumentation and methods for neurotechnology out of the lab ", Berlin Institute of Technology, doi:10.14279/depositonce-7445

A far more detailed documentation of the openNIRS hardware that is however less up to date than the openNIRS publication in frontiers can be found here:

1 Introduction 1
  1.1 Motivation 1
  1.2 Objective 2
2 State of the Technology 3
  2.1 Brief Overview of History 3
  2.2 NIRS Principle and Signals 4
  2.3 NIRS Interrogation Approaches 8
    2.3.1 Continuous Wave NIRS 8
    2.3.2 Frequency Domain NIRS 8
    2.3.3 Time Division NIRS 9
  2.4 Theoretical Background 10
  2.5 Review of existing fNIRS Technology 12
    2.5.1 NIR Light Emitters 13
    2.5.2 NIR Light Detectors 14
    2.5.3 Optical Conduction 16
    2.5.4 Signal Amplification 16
    2.5.5 Probe Designs 16
  2.6 Fields of Application 17
3 System Design 19
  3.1 Preliminary Remakrs 19
  3.2 Noise, Crosstalk and Error Sources 20
    3.2.1 Noise Errors 20
    3.2.2 Crosstalk 22
    3.2.3 Other Error Sources 22
  3.3 System Concept 23
  3.4 Hardware Design NIRS Module 26
    3.4.1 NIR Light Emitter 26
    3.4.2 NIR Light Sensor 28
    3.4.3 Amplification, Lock-In Modulation and Demodulation 30
    3.4.4 Current Regulators 34
    3.4.5 Microcontroller Unit 38
    3.4.6 General Remarks on Layout and Design 39
  3.5 Hardware Design NIRS Mainboard 40
    3.5.1 Power Supply 40
    3.5.2 Analog-to-Digital Conversion 41
    3.5.3 Communication/Bluetooth Transmission 42
    3.5.4 Microcontroller Unit 45
    3.5.5 General Remarks on Layout and Design 46
  3.6 Safety Aspects 47
  3.7 Software Design 48
    3.7.1 NIRS Module 48
    3.7.2 NIRS Mainboard 51
    3.7.3 Console Userface and Control Commands 56
    3.7.4 LabView User Interface 56
  3.8 Mechanical Design 61
    3.8.1 NIRS Module Attachment 61
    3.8.2 Mainboard and Batteries 64
4 Evaluation and Analysis 67
  4.1 Evaluation of Hardware and Design 67
    4.1.1 PWM Signal 67
    4.1.2 Power Supply 68
    4.1.3 Current Regulators 69
    4.1.4 Lock-In Detection 70
    4.1.5 System Drift 73
  4.2 Physiological Verification of the System 74
    4.2.1 Qualitative Physiological Signals: Pulse and local Blood Pressure 74
    4.2.2 BCI Trials: Mental Arithmetics 75
5 Results and Discussion 79
  5.1 System Overview 79
  5.2 Scope and Limitations 81
6 Summary and Outlook 83
A Appendix A 87
  List of Figures 103
  List of Tables 105
  References 107


To download the whole documentation as one file, please click here.