Classification of functional near-infrared spectroscopy signals corresponding to the right- and left-wrist motor imagery for development of a brain–computer interface

@article{Naseer2013ClassificationOF,
  title={Classification of functional near-infrared spectroscopy signals corresponding to the right- and left-wrist motor imagery for development of a brain–computer interface},
  author={Noman Naseer and Keum Shik Hong},
  journal={Neuroscience Letters},
  year={2013},
  volume={553},
  pages={84-89}
}

Figures and Tables from this paper

Determination of temporal window size for classifying the mean value of fNIRS signals from motor imagery
  • Noman Naseer, K. Hong
  • Biology
    2013 6th IEEE Conference on Robotics, Automation and Mechatronics (RAM)
  • 2013
TLDR
The results demonstrate the feasibility of fNIRS for a brain-computer interface and classify the functional near-infrared spectroscopy signals corresponding to right-and left-wrist motor imagery using various temporal windows of the response data.
Multiclass classification of hemodynamic responses for performance improvement of functional near-infrared spectroscopy-based brain–computer interface
TLDR
The performance of a functional near-infrared spectroscopy (fNIRS)-based brain–computer interface based on relatively short task duration and multiclass classification was improved and the bit transfer rate per minute (BPM) based on the quaternary classification accuracy was investigated.
Single-Trial Classification of fNIRS Signals in Four Directions Motor Imagery Tasks Measured From Prefrontal Cortex
TLDR
The multiclass classification of motor imagery based on fNIRS found that the orbitofrontal cortex is also involved in MI and O2sat can also serve as a classified index.
Enhancing Classification Performance of Functional Near-Infrared Spectroscopy- Brain–Computer Interface Using Adaptive Estimation of General Linear Model Coefficients
TLDR
The proposed methodology for enhanced classification of functional near-infrared spectroscopy signals utilizable in a two-class brain–computer interface (BCI) serves to demonstrate the feasibility of developing a high-classification-performance fNIRS-BCI.
Single-trial classification of fNIRS signal measured from prefrontal cortex during four directions motor imagery tasks
TLDR
The multiclass classification of motor imagery based on fNIRS found that the orbitofrontal cortex is also involved in MI and O2sat can also serve as a classified index.
Evaluating a four-class motor-imagery-based optical brain-computer interface
TLDR
This work investigates the potential of a four-class motor-imagery-based brain-computer interface (BCI) using functional near-infrared spectroscopy (fNIRS), and preliminary results suggest that this could be a viable BCI interface.
A simplified hybrid EEG-fNIRS Brain-Computer Interface for motor task classification
TLDR
The present experimental result demonstrates that the complement of EEG and fNIRS can significantly improve the classification accuracy with 3∼9% on average, and suggests the reduction of dimensionality by PCA could achieve a reduction of time complexity and computational complexity with little loss in accuracy.
Determining Optimal Feature-Combination for LDA Classification of Functional Near-Infrared Spectroscopy Signals in Brain-Computer Interface Application
TLDR
The results demonstrate the feasibility of achieving high classification accuracies using mean and peak values of HbO and HbR as features for classification of mental arithmetic vs. rest for a two-class BCI.
Classification of Hemodynamic Responses Associated With Force and Speed Imagery for a Brain-Computer Interface
TLDR
This study provided a novel paradigm for establishing fNIRS-BCI system, and provided a possibility to produce more degrees of freedom in BCI system.
...
...

References

SHOWING 1-10 OF 40 REFERENCES
Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters.
TLDR
Electroencephalogram recordings during right and left motor imagery can be used to move a cursor to a target on a computer screen and an adaptive autoregressive (AAR) model of order 6 was used for on-line classification.
Noise reduction in functional near-infrared spectroscopy signals by independent component analysis.
TLDR
The results showed the applicability of the ICA-based method to noise-contamination reduction in brain mapping by identifying the original hemodynamic response in the presence of noises.
Brain-computer interface using a simplified functional near-infrared spectroscopy system.
TLDR
This work describes the construction of the device, the principles of operation and the implementation of a fNIRS-BCI application, 'Mindswitch' that harnesses motor imagery for control, and shows that fNirS can support simple BCI functionality and shows much potential.
Towards a system-paced near-infrared spectroscopy brain-computer interface: differentiating prefrontal activity due to mental arithmetic and mental singing from the no-control state.
TLDR
Results are encouraging, and demonstrate the potential of a system-paced NIRS-BCI with one intentional control state corresponding to either mental arithmetic or mental singing, as a non-invasive brain-computer interface for individuals with severe motor impairments.
Intersession Consistency of Single-Trial Classification of the Prefrontal Response to Mental Arithmetic and the No-Control State by NIRS
TLDR
Investigation of the consistency of classification of a mental arithmetic task and a no-control condition over five experimental sessions indicates that when selecting optimal classifier training protocols for NIRS-BCI, a compromise between accuracy and convenience must be considered.
On the suitability of near-infrared (NIR) systems for next-generation brain-computer interfaces.
TLDR
This paper has used practical non-invasive optical techniques to detect characteristic haemodynamic responses due to motor imagery and consequently created an accessible BCI that is simple to attach and requires little user training.
Primary Motor and Sensory Cortex Activation during Motor Performance and Motor Imagery: A Functional Magnetic Resonance Imaging Study
TLDR
The hypothesis that MI and MP involve overlapping neural networks in perirolandic cortical areas is supported by functional magnetic resonance imaging techniques.
Classification effects of real and imaginary movement selective attention tasks on a P300-based brain-computer interface.
TLDR
Three different selective attention tasks were tested in conjunction with a P300-based protocol, showing encouraging results, showing that on average the imaginary movement achieved a P 300 versus No-P300 classification accuracy of 84.53%.
...
...