The rest period in between strength exercises determines how the short-term energy supplies in the muscles are replenished and metabolites are cleared. Near-InfraRed Spectroscopy (NIRS) is a proven method to study oxidative recovery kinetics following exercise. The goal of this study is to develop a model that predicts the oxygenated recovery state, this can help athletes optimize the resumption of exercise. 17 healthy subjects performed a sustained isometric hold in a hand gripper until volitional exertion, Tissue Saturation Index (TSI) was continuously monitored throughout and following exercise by a NIRS sensor (Train.Red PLUS). The oxygenated recovery state was manually categorized by three independent experts into four different phases of recovery; I - a pronounced increase, II - a gentle increase, III - the maximum oxygenated state, and IV - the return to baseline. A Recurrent Neural Network, inspired by Natural Language Processing, was trained and tested on this data, resulting in a model that predicts shifts between phases of recovery. A 5-fold cross-validation analysis resulted in the following average performance: • Recurrent Neural Network: Accuracy: 55.17%, categorical cross-entropy: 1.02351. • Multi-Layer Perceptron: Accuracy: 57.16%, categorical cross-entropy: 0.95201. • XGBoost: accuracy: 44.85%, categorical cross-entropy: 10.1119. In predicting the user’s current state of oxygenated recovery the MLP and RNN are similar in performance, however, the MLP shows erratic behavior, while the RNN generally follows the shift in phases of the ground truth. These capabilities could enable athletes with different fitness goals to design goal-tailored and therefore more efficient training.

Objective: Respiration is recognized as a systematic physiological interference in functional near-infrared spectroscopy (fNIRS). However, it remains unanswered as to whether it is possible to estimate the respiratory rate (RR) from such interference. Undoubtedly, RR estimation from fNIRS can provide complementary information that can be used alongside the cerebral activity analysis, e.g., sport studies. Thus, the objective of this paper is to propose a method for RR estimation from fNIRS. Our primary presumption is that changes in the baseline wander of oxygenated hemoglobin concentration (O2Hb) signal are related to RR. Methods: fNIRS and respiratory signals were concurrently collected from subjects during controlled breathing tasks at a constant rate from 0.1 Hz to 0.4 Hz. Firstly, the signal quality index algorithm is employed to select the best O2Hb signal, and then a band-pass filter with cut-off frequencies from 0.05 to 2 Hz is used to remove very low- and high-frequency artifacts. Secondly, troughs of the filtered O2Hb signal are localized for synthesizing the baseline wander (S1) using cubic spline interpolation. Finally, the fast Fourier transform of the S1 signal is computed, and its dominant frequency is considered as RR. In this paper, two different datasets were employed, where the first one was used for the parameter adjustment of the proposed method, and the second one was solely used for testing. Results: The low mean absolute error between the reference and estimated RRs for the first and second datasets (2.6 and 1.3 breaths per minute, respectively) indicates the feasibility of the proposed method for RR estimation from fNIRS. Significance: This paper provides a novel view on the respiration interference as a source of complementary information in fNIRS.

Recent studies have established that cardiac and respiratory phases can modulate perception and related neural dynamics. While heart rate and respiratory sinus arrhythmia possibly affect interoception biomarkers, such as heartbeat-evoked potentials, the relative changes in heart rate and cardiorespiratory dynamics in interoceptive processes have not yet been investigated. In this study, we investigated the variation in heart and breathing rates, as well as higher functional dynamics including cardiorespiratory correlation and frontal hemodynamics measured with fNIRS, during a heartbeat counting task. To further investigate the functional physiology linked to changes in vagal activity caused by specific breathing rates, we performed the heartbeat counting task together with a controlled breathing rate task. The results demonstrate that focusing on heartbeats decreases breathing and heart rates in comparison, which may be part of the physiological mechanisms related to “listening” to the heart, the focus of attention, and self-awareness. Focusing on heartbeats was also observed to increase frontal connectivity, supporting the role of frontal structures in the neural monitoring of visceral inputs. However, cardiorespiratory correlation is affected by both heartbeats counting and controlled breathing tasks. Based on these results, we concluded that variations in heart and breathing rates are confounding factors in the assessment of interoceptive abilities and relative fluctuations in breathing and heart rates should be considered to be a mode of covariate measurement of interoceptive processes.

Increased oxygenated hemoglobin concentration of the prefrontal cortex (PFC) has been observed during linear walking, particularly when there is a high attention demand on the task, like in dual-task (DT) paradigms. Despite the knowledge that cognitive and motor demands depend on the complexity of the motor task, most studies have only focused on usual walking, while little is known for more challenging tasks, such as curved paths. To explore the relationship between cortical activation and gait biomechanics, 20 healthy young adults were asked to perform linear and curvilinear walking trajectories in single-task and DT conditions. PFC activation was assessed using functional near-infrared spectroscopy, while gait quality with four inertial measurement units. The Figure-of-8-Walk-Test was adopted as the curvilinear trajectory, with the "Serial 7s" test as concurrent cognitive task. Results show that walking along curvilinear trajectories in DT led to increased PFC activation and decreased motor performance. Under DT walking, the neural correlates of executive function and gait control tend to be modified in response to the cognitive resources imposed by the motor task. Being more representative of real-life situations, this approach to curved walking has the potential to reveal crucial information and to improve people' s balance, safety, and life's quality.

Functional near infrared spectroscopy (fNIRS) is used for brain hemodynamic assessment. Cortical hemodynamics are reliably estimated when the recorded signal has a sufficient quality. This is acquired when fNIRS optodes have proper scalp coupling. A lack of proper scalp coupling causes false positives and false negatives. Therefore, developing an objective algorithm for determining fNIRS signal quality is of great importance. In this study, we developed a machine learning-based algorithm for quantitatively rating fNIRS signal quality. Our promising results confirm the efficacy of the algorithm in determining fNIRS signal quality and hence decreasing misinterpretations.

We propose the signal quality index (SQI) algorithm as a novel tool for quantitatively assessing the functional near infrared spectroscopy (fNIRS) signal quality in a numeric scale from 1 (very low quality) to 5 (very high quality). The algorithm comprises two preprocessing steps followed by three consecutive rating stages. The results on a dataset annotated by independent fNIRS experts showed SQI performed significantly better (p<0.05) than PHOEBE (placing headgear optodes efficiently before experimentation) and SCI (scalp coupling index), two existing algorithms, in both quantitatively rating and binary classifying the fNIRS signal quality. Employment of the proposed algorithm to estimate the signal quality before processing the fNIRS signals increases certainty in the interpretations.

Communication is critical for autonomy, social interaction and decision-making. It is defined as one of the basic human's needs. People with speech impairments, caused by different pathologies, often face accessibility issues to augmentative and alternative communication systems, especially in cases with severe motor impairment co-morbidity. Brain- computer interfaces (BCIs) have been assessed as accessibility devices in cases of total motor impairment. However, the lack of validation in a real environment with target users is holding back their development as daily life assistive technologies for these potential users.We designed and prototyped a BCI based on Steady State Visual Evoked Potentials (SSVEP) to be used as input to an alternative communication system, and proposed a validation protocol that involves the interference of neighboring stimuli and environmental noise. We report our parameters assessment to elicit an optimal response of SSVEP, and our hardware and software design based on OpenBCI technology, describing our first test results comparing system performance with a healthy user and a user with Amyotrophic Lateral Sclerosis (ALS).