scholarly journals Virtual and Actual Humanoid Robot Control with Four-Class Motor-Imagery-Based Optical Brain-Computer Interface

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Alyssa M. Batula ◽  
Youngmoo E. Kim ◽  
Hasan Ayaz
Keyword(s):  
Motor Imagery ◽  
Robot Control ◽  
Humanoid Robot ◽  
Near Infrared ◽  
Functional Near Infrared Spectroscopy ◽  
Activation Patterns ◽  
Left Turn ◽  
Left Hand ◽  
Real Robot ◽  
High Level

Motor-imagery tasks are a popular input method for controlling brain-computer interfaces (BCIs), partially due to their similarities to naturally produced motor signals. The use of functional near-infrared spectroscopy (fNIRS) in BCIs is still emerging and has shown potential as a supplement or replacement for electroencephalography. However, studies often use only two or three motor-imagery tasks, limiting the number of available commands. In this work, we present the results of the first four-class motor-imagery-based online fNIRS-BCI for robot control. Thirteen participants utilized upper- and lower-limb motor-imagery tasks (left hand, right hand, left foot, and right foot) that were mapped to four high-level commands (turn left, turn right, move forward, and move backward) to control the navigation of a simulated or real robot. A significant improvement in classification accuracy was found between the virtual-robot-based BCI (control of a virtual robot) and the physical-robot BCI (control of the DARwIn-OP humanoid robot). Differences were also found in the oxygenated hemoglobin activation patterns of the four tasks between the first and second BCI. These results corroborate previous findings that motor imagery can be improved with feedback and imply that a four-class motor-imagery-based fNIRS-BCI could be feasible with sufficient subject training.

scholarly journals Comparison of Brain Activation during Motor Imagery and Motor Movement Using fNIRS

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Alyssa M. Batula ◽  
Jesse A. Mark ◽  
Youngmoo E. Kim ◽  
Hasan Ayaz
Keyword(s):  
Motor Imagery ◽  
Near Infrared ◽  
Brain Activity ◽  
Natural Extension ◽  
Cortical Activation ◽  
Lower Limbs ◽  
Motor Execution ◽  
Functional Near Infrared Spectroscopy ◽  
Left Hand ◽  
Motor Movement

Motor-activity-related mental tasks are widely adopted for brain-computer interfaces (BCIs) as they are a natural extension of movement intention, requiring no training to evoke brain activity. The ideal BCI aims to eliminate neuromuscular movement, making motor imagery tasks, or imagined actions with no muscle movement, good candidates. This study explores cortical activation differences between motor imagery and motor execution for both upper and lower limbs using functional near-infrared spectroscopy (fNIRS). Four simple finger- or toe-tapping tasks (left hand, right hand, left foot, and right foot) were performed with both motor imagery and motor execution and compared to resting state. Significant activation was found during all four motor imagery tasks, indicating that they can be detected via fNIRS. Motor execution produced higher activation levels, a faster response, and a different spatial distribution compared to motor imagery, which should be taken into account when designing an imagery-based BCI. When comparing left versus right, upper limb tasks are the most clearly distinguishable, particularly during motor execution. Left and right lower limb activation patterns were found to be highly similar during both imagery and execution, indicating that higher resolution imaging, advanced signal processing, or improved subject training may be required to reliably distinguish them.

scholarly journals Comparison of Brain Activation Patterns during Olfactory Stimuli between Recovered COVID-19 Patients and Healthy Controls: A Functional Near-Infrared Spectroscopy (fNIRS) Study

2021 ◽  
Vol 11 (8) ◽  
pp. 968
Author(s):  
Roger C. Ho ◽  
Vijay K. Sharma ◽  
Benjamin Y. Q. Tan ◽  
Alison Y. Y. Ng ◽  
Yit-Shiang Lui ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Time Interval ◽  
Healthy Controls ◽  
Functional Near Infrared Spectroscopy ◽  
Activation Patterns ◽  
Identification Test ◽  
Olfactory Stimuli ◽  
Preliminary Study

Impaired sense of smell occurs in a fraction of patients with COVID-19 infection, but its effect on cerebral activity is unknown. Thus, this case report investigated the effect of COVID-19 infection on frontotemporal cortex activity during olfactory stimuli. In this preliminary study, patients who recovered from COVID-19 infection (n = 6) and healthy controls who never contracted COVID-19 (n = 6) were recruited. Relative changes in frontotemporal cortex oxy-hemoglobin during olfactory stimuli was acquired using functional near-infrared spectroscopy (fNIRS). The area under curve (AUC) of oxy-hemoglobin for the time interval 5 s before and 15 s after olfactory stimuli was derived. In addition, olfactory function was assessed using the Sniffin’ Sticks 12-identification test (SIT-12). Patients had lower SIT-12 scores than healthy controls (p = 0.026), but there were no differences in oxy-hemoglobin AUC between healthy controls and patients (p > 0.05). This suggests that past COVID-19 infection may not affect frontotemporal cortex function, and these preliminary results need to be verified in larger samples.

scholarly journals Comparison of Activation in the Prefrontal Cortex of Native Speakers of Mandarin by Ability of Japanese as a Second Language Using a Novel Speaking Task

Healthcare ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 412
Author(s):  
Li Cong ◽  
Hideki Miyaguchi ◽  
Chinami Ishizuki
Keyword(s):  
Second Language ◽  
Prefrontal Cortex ◽  
Cognitive Load ◽  
Near Infrared ◽  
Native Speakers ◽  
Brain Activation ◽  
Brain Regions ◽  
Strong Inhibition ◽  
Functional Near Infrared Spectroscopy ◽  
High Level

Evidence shows that second language (L2) learning affects cognitive function. Here in this work, we compared brain activation in native speakers of Mandarin (L1) who speak Japanese (L2) between and within two groups (high and low L2 ability) to determine the effect of L2 ability in L1 and L2 speaking tasks, and to map brain regions involved in both tasks. The brain activation during task performance was determined using prefrontal cortex blood flow as a proxy, measured by functional near-infrared spectroscopy (fNIRS). People with low L2 ability showed much more brain activation when speaking L2 than when speaking L1. People with high L2 ability showed high-level brain activation when speaking either L2 or L1. Almost the same high-level brain activation was observed in both ability groups when speaking L2. The high level of activation in people with high L2 ability when speaking either L2 or L1 suggested strong inhibition of the non-spoken language. A wider area of brain activation in people with low compared with high L2 ability when speaking L2 is considered to be attributed to the cognitive load involved in code-switching L1 to L2 with strong inhibition of L1 and the cognitive load involved in using L2.

scholarly journals fNIRS Complexity Analysis for the Assessment of Motor Imagery and Mental Arithmetic Tasks

Entropy ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. 761
Author(s):  
Ameer Ghouse ◽  
Mimma Nardelli ◽  
Gaetano Valenza
Keyword(s):  
Motor Imagery ◽  
Near Infrared ◽  
Complexity Analysis ◽  
Mental Arithmetic ◽  
Hemoglobin Concentration ◽  
Linear Analysis ◽  
Mean Value ◽  
Functional Near Infrared Spectroscopy ◽  
Physiological Processes ◽  
Concentration Time Series

Conventional methods for analyzing functional near-infrared spectroscopy (fNIRS) signals primarily focus on characterizing linear dynamics of the underlying metabolic processes. Nevertheless, linear analysis may underrepresent the true physiological processes that fully characterizes the complex and nonlinear metabolic activity sustaining brain function. Although there have been recent attempts to characterize nonlinearities in fNIRS signals in various experimental protocols, to our knowledge there has yet to be a study that evaluates the utility of complex characterizations of fNIRS in comparison to standard methods, such as the mean value of hemoglobin. Thus, the aim of this study was to investigate the entropy of hemoglobin concentration time series obtained from fNIRS signals and perform a comparitive analysis with standard mean hemoglobin analysis of functional activation. Publicly available data from 29 subjects performing motor imagery and mental arithmetics tasks were exploited for the purpose of this study. The experimental results show that entropy analysis on fNIRS signals may potentially uncover meaningful activation areas that enrich and complement the set identified through a traditional linear analysis.

Sign in / Sign up

Export Citation Format

Share Document