Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain-computer interface

By Ranganatha Sitaram, Haihong Zhang, Cuntai Guan, M. Thulasidas, Yoko Hoshi, A. Ishikawa, K. Shimizu, Niels Birbaumer. NeuroImage, 2007

610 citations recorded by the source corpus.

There has been an increase in research interest for brain-computer interface (BCI) technology as an alternate mode of communication and environmental control for the disabled, such as patients suffering from amyotrophic lateral sclerosis (ALS), brainstem stroke and spinal cord injury. Disabled patients with appropriate physical care and cognitive ability to communicate with their social environment continue to live with a reasonable quality of life over extended periods of time. Near-infrared spectroscopy is a non-invasive technique which utilizes light in the near-infrared range (700 to 1000 nm) to determine cerebral oxygenation, blood flow and metabolic status of localized regions of the brain. In this paper, we describe a study conducted to test the feasibility of using multichannel NIRS in the development of a BCI. We used a continuous wave 20-channel NIRS system over the motor cortex of 5 healthy volunteers to measure oxygenated and deoxygenated hemoglobin changes during left-hand and right-hand motor imagery. We present results of signal analysis indicating that there exist distinct patterns of hemodynamic responses which could be utilized in a pattern classifier towards developing a BCI. We applied two different pattern recognition algorithms separately, Support Vector Machines (SVM) and Hidden Markov Model (HMM), to classify the data offline. SVM classified left-hand imagery from right-hand imagery with an average accuracy of 73% for all volunteers, while HMM performed better with an average accuracy of 89%. Our results indicate potential application of NIRS in the development of BCIs. We also discuss here future extension of our system to develop a word speller application based on a cursor control paradigm incorporating online pattern classification of single-trial NIRS data.

Subfields: Biomedical Engineering, Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/d67362dbae92e3dc19b11c2af6b2436b288d80e2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Brain Computer Interfaces, a Review (2012, background): “Some studies have already demonstrated the feasibility of mental task detection through NIRS-derived optical responses [93,99,100].”
NIRS-Based Hyperscanning Reveals Increased Interpersonal Coherence in Superior Frontal Cortex during Cooperation (2011, neutral): “NIRS is a relatively flexible technology and to date has been successfully applied in several domains [22], including physiological mechanisms of the BOLD response [23-31], non-invasive brain-computer interfaces [32-36], and brain activity in both active tasks and resting states [37-42].”
A quantitative comparison of NIRS and fMRI across multiple cognitive tasks (2010, methodology): “NIRS can be very portable (Atsumori et al., 2009) and has been proposed as a useful technology for non-invasive brain–computer interfaces (Power et al., 2010; Coyle et al., 2004, 2007; Sitaram et al., 2007; Utsugi et al., 2008).”
Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects (2009, background): “In recent years, there has been growing interest in BCIs based on metabolic signals derived from the brain [57]–[69].”
A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology (2014, background): “…Toronov, 2012) or principal component analysis (PCA) (Franceschini et al., 2006; H. Zhang et al., 2010; H. Zhang et al., 2011; Mäki et al., 2010; Sitaram et al., 2007; Soe and Nakagawa, 2008; Virtanen et al., 2012; Y. Zhang et al., 2005) were also developed to address the obstacle of how to…”
The brain–computer interface cycle (2009, background): “The study in [29] was one of the first to demonstrate BCI control based on NIRS.”
fNIRS-based brain-computer interfaces: a review (2015, methodology): “…using a simple low-pass, or a high-pass, or a band-pass filtering to remove physiological noises (Coyle et al., 2004, 2007; Naito et al., 2007; Sitaram et al., 2007; Bauernfeind et al., 2008; Luu and Chau, 2009; Power et al., 2010, 2011; Hu et al., 2012; Liu et al., 2013; Hong et al., 2015).”
Brain–computer interfaces: communication and restoration of movement in paralysis (2007, methodology): “…et al. 2004), extracellular synaptic field potentials from implanted electrodes, metabolic brain activity, and the BOLD response recorded through functional magnetic resonance imaging (fMRI) and blood oxygenation with near infrared spectroscopy (NIRS) (Weiskopf et al. 2007; Sitaram et al. 2007a).”
The Hybrid BCI (2010, background): “A brain switch can also rely on SSVEPs with a high amplitude threshold (Cheng et al., 2002) or hemodynamic changes measured through NIRS (Coyle et al., 2007).”
Real-time fMRI neurofeedback: Progress and challenges (2013, background): “…with saliva pH-changes (Wilhelm et al., 2006), sniffing (Plotkin et al., 2010), functional near infrared spectroscopy (fNIRS; Naito et al., 2007; Sitaram et al., 2007) and invasive recordings (Hochberg et al., 2006; Ramos Murguialday et al., 2009) cannot overcome the described psychological…”
Brain-Computer Interfaces: Beyond Medical Applications (2012, neutral): “NIRS could be such an additional sensor [11].”
Real-time fMRI and its application to neurofeedback (2012, background): “To reduce costs and increase accessibility and portability (Min et al., 2010), near infrared spectroscopy (NIRS) may be complemented with rtfMRI (Sitaram et al., 2007), since it can also measure the BOLD effect though only in cortical areas.”
Mental workload during n-back task—quantified in the prefrontal cortex using fNIRS (2014, methodology): “The most frequently used paradigm is motor-imagery (Sitaram et al., 2007).”