Hand Motion Detection in fNIRS Neuroimaging Data

A Comparison of Immersive vs. Non-Immersive Virtual Reality Exercises for the Upper Limb: A Functional Near-Infrared Spectroscopy Pilot Study with Healthy Participants

Functional near-infrared spectroscopy (fNIRS) allows for a reliable assessment of oxygenated blood flow in relevant brain regions. Recent advancements in immersive virtual reality (VR)-based technology have generated many new possibilities for its application, such as in stroke rehabilitation. In this study, we asked whether there is a difference in oxygenated hemoglobin (HbO2) within brain motor areas during hand/arm movements between immersive and non-immersive VR settings. Ten healthy young participants (24.3 ± 3.7, three females) were tested using a specially developed VR paradigm, called "bus riding", whereby participants used their hand to steer a moving bus. Both immersive and non-immersive conditions stimulated brain regions controlling hand movements, namely motor cortex, but no significant differences in HbO2 could be found between the two conditions in any of the relevant brain regions. These results are to be interpreted with caution, as only ten participants were included in the study.

Neural Substrates for Hand and Shoulder Movement in Healthy Adults: A Functional near Infrared Spectroscopy Study

Characterization of cortical activation patterns during movements in healthy adults may help our understanding of how the injured brain works. Upper limb motor tasks are commonly used to assess impaired motor function and to predict recovery in individuals with neurological disorders such as stroke. This study aimed to explore cortical activation patterns associated with movements of the hand and shoulder using functional near-infrared spectroscopy (fNIRS) and to demonstrate the potential of this technology to distinguish cerebral activation between distal and proximal movements. Twenty healthy, right-handed participants were recruited. Two 10-s motor tasks (right-hand opening-closing and right shoulder abduction-adduction) were performed in a sitting position at a rate of 0.5 Hz in a block paradigm. We measured the variations in oxyhemoglobin (HbO₂) and deoxyhemoglobin (HbR) concentrations. fNIRS was performed with a 24-channel system (Brite 24®; Artinis) that covered most motor control brain regions bilaterally. Activation was mostly contralateral for both hand and shoulder movements. Activation was more lateral for hand movements and more medial for shoulder movements, as predicted by the classical homunculus representation. Both HbO₂ and HbR concentrations varied with the activity. Our results showed that fNIRS can distinguish patterns of cortical activity in upper limb movements under ecological conditions. These results suggest that fNIRS can be used to measure spontaneous motor recovery and rehabilitation-induced recovery after brain injury. The trial was restropectively registered on January 20, 2023: NCT05691777 (clinicaltrial.gov).

Wearable Multisensor Ring-Shaped Probe for Assessing Stress and Blood Oxygenation: Design and Preliminary Measurements

The increasing interest in innovative solutions for health and physiological monitoring has recently fostered the development of smaller biomedical devices. These devices are capable of recording an increasingly large number of biosignals simultaneously, while maximizing the user's comfort. In this study, we have designed and realized a novel wearable multisensor ring-shaped probe that enables synchronous, real-time acquisition of photoplethysmographic (PPG) and galvanic skin response (GSR) signals. The device integrates both the PPG and GSR sensors onto a single probe that can be easily placed on the finger, thereby minimizing the device footprint and overall size. The system enables the extraction of various physiological indices, including heart rate (HR) and its variability, oxygen saturation (SpO₂), and GSR levels, as well as their dynamic changes over time, to facilitate the detection of different physiological states, e.g., rest and stress. After a preliminary SpO₂ calibration procedure, measurements have been carried out in laboratory on healthy subjects to demonstrate the feasibility of using our system to detect rapid changes in HR, skin conductance, and SpO₂ across various physiological conditions (i.e., rest, sudden stress-like situation and breath holding). The early findings encourage the use of the device in daily-life conditions for real-time monitoring of different physiological states.

Effects of Background Music on Mental Fatigue in Steady-State Visually Evoked Potential-Based BCIs

As a widely used brain–computer interface (BCI) paradigm, steady-state visually evoked potential (SSVEP)-based BCIs have the advantages of high information transfer rates, high tolerance for artifacts, and robust performance across diverse users. However, the incidence of mental fatigue from prolonged, repetitive stimulation is a critical issue for SSVEP-based BCIs. Music is often used as a convenient, non-invasive means of relieving mental fatigue. This study investigates the compensatory effect of music on mental fatigue through the introduction of different modes of background music in long-duration, SSVEP-BCI tasks. Changes in electroencephalography power index, SSVEP amplitude, and signal-to-noise ratio were used to assess participants’ mental fatigue. The study’s results show that the introduction of exciting background music to the SSVEP-BCI task was effective in relieving participants’ mental fatigue. In addition, for continuous SSVEP-BCI tasks, a combination of musical modes that used soothing background music during the rest interval phase proved more effective in reducing users’ mental fatigue. This suggests that background music can provide a practical solution for long-duration SSVEP-based BCI implementation.

Motion artifacts removal and evaluation techniques for functional near-infrared spectroscopy signals: A review

With the emergence of an increasing number of functional near-infrared spectroscopy (fNIRS) devices, the significant deterioration in measurement caused by motion artifacts has become an essential research topic for fNIRS applications. However, a high requirement for mathematics and programming limits the number of related researches. Therefore, here we provide the first comprehensive review for motion artifact removal in fNIRS aiming to (i) summarize the latest achievements, (ii) present the significant solutions and evaluation metrics from the perspective of application and reproduction, and (iii) predict future topics in the field. The present review synthesizes information from fifty-one journal articles (screened according to three criteria). Three hardware-based solutions and nine algorithmic solutions are summarized, and their application requirements (compatible signal types, the availability for online applications, and limitations) and extensions are discussed. Five metrics for noise suppression and two metrics for signal distortion were synthesized to evaluate the motion artifact removal methods. Moreover, we highlight three deficiencies in the existing research: (i) The balance between the use of auxiliary hardware and that of an algorithmic solution is not clarified; (ii) few studies mention the filtering delay of the solutions, and (iii) the robustness and stability of the solution under extreme application conditions are not discussed.

Searching for the Mechanism of Action of Extremely Low Frequency Electromagnetic Field-The Pilot fNIRS Research

There is an ongoing debate on the benefits of magnetic stimulation in neurological disorders. Objectives: We aimed to evaluate the influence of magnetic stimulation on blood oxygenation of the motor cortex using functional near-infrared spectroscopy (fNIRS). Methods: A total of 16 healthy volunteer participants were subjected to four protocols. In the first two protocols, the participants remained at rest without (and then with) magnetic stimulation. In the next two protocols, motor cortex stimulation was achieved using a finger-tapping task, with and without magnetic stimulation. Changes in blood oxygenation levels within the motor cortex were recorded and analysed. Results: No characteristic changes in the blood oxygenation level-dependent responses were observed in resting participants after magnetic stimulation. No statistically significant difference was observed in the amplitude of the fNIRS signal before and after magnetic stimulation. We observed characteristic blood oxygenation level-dependent responses after the finger-tapping task in the second protocol, but not after magnetic stimulation. Conclusions: Although we did not observe any measurable effect of the magnetic field on the haemodynamic response of the motor cortex, understanding the mechanism(s) of magnetic stimulation may be important. Additional, detailed studies are needed to prove or negate the potential of this medical procedure.

Motor Cortex Activation During Writing in Focal Upper-Limb Dystonia: An fNIRS Study

BACKGROUND

Functional imaging studies have associated dystonia with abnormal activation in motor and sensory brain regions. Commonly used techniques such as functional magnetic resonance imaging impose physical constraints, limiting the experimental paradigms. Functional near-infrared spectroscopy (fNIRS) offers a new noninvasive possibility for investigating cortical areas and the neural correlates of complex motor behaviors in unconstrained settings.

METHODS

We compared the cortical brain activation of patients with focal upper-limb dystonia and controls during the writing task under naturalistic conditions using fNIRS. The primary motor cortex (M1), the primary somatosensory cortex (S1), and the supplementary motor area were chosen as regions of interest (ROIs) to assess differences in changes in both oxyhemoglobin (oxy-Hb) and deoxyhemoglobin (deoxy-Hb) between groups.

RESULTS

Group average activation maps revealed an expected pattern of contralateral recruitment of motor and somatosensory cortices in the control group and a more bilateral pattern of activation in the dystonia group. Between-group comparisons focused on specific ROIs revealed an increased activation of the contralateral M1 and S1 cortices and also of the ipsilateral M1 cortex in patients.

CONCLUSIONS

Overactivity of contralateral M1 and S1 in dystonia suggest a reduced specificity of the task-related cortical areas, whereas ipsilateral activation possibly indicates a primary disorder of the motor cortex or an endophenotypic pattern. To our knowledge, this is the first study using fNIRS to assess cortical activity in dystonia during the writing task under natural settings, outlining the potential of this technique for monitoring sensory and motor retraining in dystonia rehabilitation.

Fugl-Meyer hand motor imagination recognition for brain–computer interfaces using only fNIRS

As a relatively new physiological signal of brain, functional near-infrared spectroscopy (fNIRS) is being used more and more in brain–computer interface field, especially in the task of motor imagery. However, the classification accuracy based on this signal is relatively low. To improve the accuracy of classification, this paper proposes a new experimental paradigm and only uses fNIRS signals to complete the classification task of six subjects. Notably, the experiment is carried out in a non-laboratory environment, and movements of motion imagination are properly designed. And when the subjects are imagining the motions, they are also subvocalizing the movements to prevent distraction. Therefore, according to the motor area theory of the cerebral cortex, the positions of the fNIRS probes have been slightly adjusted compared with other methods. Next, the signals are classified by nine classification methods, and the different features and classification methods are compared. The results show that under this new experimental paradigm, the classification accuracy of 89.12% and 88.47% can be achieved using the support vector machine method and the random forest method, respectively, which shows that the paradigm is effective. Finally, by selecting five channels with the largest variance after empirical mode decomposition of the original signal, similar classification results can be achieved.

Analyzing Lung Disease Using Highly Effective Deep Learning Techniques

Image processing technologies and computer-aided diagnosis are medical technologies used to support decision-making processes of radiologists and medical professionals who provide treatment for lung disease. These methods involve using chest X-ray images to diagnose and detect lung lesions, but sometimes there are abnormal cases that take some time to occur. This experiment used 5810 images for training and validation with the MobileNet, Densenet-121 and Resnet-50 models, which are popular networks used to classify the accuracy of images, and utilized a rotational technique to adjust the lung disease dataset to support learning with these convolutional neural network models. The results of the convolutional neural network model evaluation showed that Densenet-121, with a state-of-the-art Mish activation function and Nadam-optimized performance. All the rates for accuracy, recall, precision and F1 measures totaled 98.88%. We then used this model to test 10% of the total images from the non-dataset training and validation. The accuracy rate was 98.97% for the result which provided significant components for the development of a computer-aided diagnosis system to yield the best performance for the detection of lung lesions.