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Averaging amplitudes over consecutive time samples (i.e., time window) is widely used to calculate the peak amplitude of event-related potentials (ERPs). Cluster analysis of the spatio-temporal ERP data is a promising tool to determine the time window of an ERP of interest. However, determining an appropriate number of clusters to optimally represent ERPs is still challenging. Here, we develop a new method to estimate the optimal number of clusters utilizing consensus clustering. Various polarity dependent clustering methods, namely, k-means, hierarchical clustering, fuzzy c-means, self-organizing map, spectral clustering, and Gaussian mixture model, are used to configure consensus clustering after assessing them individually. When a range of clusters is applied many times, the optimal number of clusters should correspond to the expectation, which is the average of the obtained mean inner-similarities of estimated time windows across all conditions and groups converge in the satisfactory thresholds. In order to assess our method, the proposed method has been applied to simulated data and prospective memory experiment ERP data aimed to qualify N2 and P3, and N300 and prospective positivity components, respectively. The results of determining the optimal number of clusters meet at six cluster maps for both ERP data. In addition, our results revealed that the proposed method could be reliably applied to ERP data to determine the appropriate time window for the ERP of interest when the measurement interval is not accurately defined.

Background: A great part of brain-computer interfaces (BCI’s) research has so far, been focusing on interactions using exogenous responses under selective attention with synchronous and reactive methods for clear relation with controlled stimuli and the widespread applicability of the methods. Over the two past decades, event-related potentials (ERP’s) have become more and more investigated for a broader community of researchers due to the relatively little amount of training necessary for a system to perform and their detection across diverse modalities of acquisition to correlate with the sensory discrimination of dedicated stimuli. The proposed study aims to observe the detection of ERP’s components and correlated neuralphenomena under the visual presentation of complex stimuli and devise processing methods that would generalize their classification for applications in computer-aided architectural design (CAAD), where visual complexity becomes an intrinsic feature of the tasks.Methods: Its objective is exploratory and twofold: evaluating data processing and stimulus presentation methods, as well as the evolution of similar responses in repeated measures intra- and inter-subjects in non-clinical states. The study is divided into 4 phases of offline-online experiments across its timeline. Offline experiments are used to study and validate base methods, and online experiments are used to validate their modifications for application usage. Each of these experiments are framed by cross-sectional and longitudinal sub-studies. The chosen neural phenomena and study, as well as the presentation paradigm, precondition the trial design with repeated measures for averaging temporal waveforms within an exploratory framework. An expected total of at least 200 participants will be recruited over the course of the study. The aim is to recruit for a distribution of age, gender and participants that are involved in studies or practices of architecture, visual arts or related to maximize statistical significance regarding the targeted population segment.Discussion: This study aims to investigate the cardinality of discriminative neural patterns correlated with the presentation of complex visual stimuli by detecting subcomponents of these neural phenomena using the designed system on short and prolonged periods of time and involving participants from architecture, visual arts or related fields. Subsequent results will bring to further discussion the role of visual experience in such system and the range it might address in the population segment.Trial Status: The first phase of the study has been submitted for non-clinical trials and approved by ETH Z¨urich Ethics Commission on the 02nd July 2021 and given the registration ID: EK 2021-N-106. The first trial has been approved on August 1st 2021

Background Music therapy improves neuronal activity and connectivity of healthy persons and patients with clinical symptoms of neurological diseases like Parkinson’s disease, Alzheimer’s disease, and major depression. Despite the plethora of publications that have reported the positive effects of music interventions, little is known about how music improves neuronal activity and connectivity in afflicted patients. Methods For patients suffering from Parkinson’s disease (PD), we propose a daily 25-min music-based synchronous finger tapping (SFT) intervention for 8 weeks. Eligible participants with PD are split into two groups: an intervention group and a control arm. In addition, a third cohort of healthy controls will be recruited. Assessment of finger tapping performances, the Unified Parkinson’s Disease Rating Scale (UPDRS), an n-back test, the Montreal Cognitive Assessment (MoCA), as well as oxygenated hemoglobin (HbO2), deoxygenated hemoglobin (HbR), and total hemoglobin activation collected by functional near-infrared spectroscopy (fNIRS) are measured at baseline, week 4 (during), week 8 (post), and week 12 (retention) of the study. Data collected from the two PD groups are compared to baseline performances from healthy controls. Discussion This exploratory prospective trial study investigates the cortical neuronal activity and therapeutic effects associated with an auditory external cue used to induce automatic and implicit synchronous finger tapping in patients diagnosed with PD. The extent to which the intervention is effective may be dependent on the severity of the disease. The study’s findings are used to inform larger clinical studies for optimization and further exploration of the therapeutic effects of movement-based music therapy on neural activity in neurological diseases. Trial registration ClinicalTrials.gov NCT04212897. Registered on December 30, 2019. The participant recruitment and study protocol have received ethical approval from the First Affiliated Hospital of Dalian Medical University. The hospital Protocol Record number is PJ-KY-2019-123. The protocol was named “fNIRS Studies of Music Intervention of Parkinson’s Disease.” The current protocol is version 1.1, revised on September 1, 2020.

Background: Music therapy improves neuronal activity and connectivity of healthy persons and patients with clinical symptoms of neurological diseases like Parkinson’s Disease, Alzheimer’s Disease, and Major Depression. Despite the plethora of publications that have reported the positive effects of music interventions, little is known about how music improves neuronal activity and connectivity in afflicted patients. In this three-part series of studies, each of the three neurological disorders – (1) Parkinson’s Disease, (2) Alzheimer’s Disease, and (3) Major Depression – will be treated individually in detail. Methods: For patients suffering from Parkinson’s Disease (PD), we propose a daily 25-minute music-based synchronous finger tapping (SFT) intervention for 8-weeks. Participants will be each split into two groups: an intervention group and a control arm. Assessment of finger tapping performances, the Unified Parkinson’s Disease Rating Scale (UPDRS), an n-back test, the Montreal Cognitive Assessment (MoCA), as well as oxygenated- (HbO 2 ), deoxygenated- hemoglobin (HbR), and total hemoglobin activation collected by functional near-infrared spectroscopy (fNIRS) will be measured at baseline, week 4 (during), week 8 (post), and week 12 (retention) of the study. Data collected from the two PD groups will be compared to baseline performances from healthy controls.Discussion: The trial study investigates the cortical neuronal activity and therapeutic effects associated with an auditory external cue used to induce automatic and implicit synchronous finger tapping in patients diagnosed with PD. The extent to which the intervention is effective may be dependent on the severity of the disease.

Functional near-infrared spectroscopy (fNIRS) is one of the latest noninvasive brain function measuring technique that has been used for the purpose of brain-computer interfacing (BCI). In this paper, we compare and analyze the effect of six most commonly used filtering techniques (i.e., Gaussian, Butterworth, Kalman, hemodynamic response filter (hrf), Wiener, and finite impulse response) on classification accuracies of fNIRS-BCI. To conclude with the best optimal filter for a specific cortical task owing to a specific cortical region, we divided our experimental tasks according to the three main cortical regions: prefrontal, motor, and visual cortex. Three different experiments were performed for prefrontal and motor execution tasks while one for visual stimuli. The tasks performed for prefrontal include rest (R) vs mental arithmetic (MA), R vs object rotation (OB), and OB vs MA. Similarly, for motor execution, R vs left finger tapping (LFT), R vs right finger tapping (RFT), and LFT vs RFT. Likewise, for the visual cortex, R vs visual stimuli (VS) task. These experiments were performed for ten trials with five subjects. For consistency among extracted data, six statistical features were evaluated using oxygenated hemoglobin, namely, slope, mean, peak, kurtosis, skewness, and variance. Combination of these six features was used to classify data by the nonlinear support vector machine (SVM). The classification accuracies obtained from SVM by using hrf and Gaussian were significantly higher for R vs MA, R vs OB, R vs RFT, and R vs VS and Wiener filter for OB vs MA. Similarly, for R vs LFT and LFT vs RFT, hrf was found to be significant p<0.05. These results show the feasibility of using hrf for effective removal of noises from fNIRS data.

Traffic signals are vital for traffic management. It is essential to increase the efficiency of the traffic controller to alleviate traffic congestion. In this paper, the traffic light control system using fuzzy logic is designed to minimize queue count (QC) and waiting time (WT) for vehicles at the intersection. Real-time traffic is generated and a fuzzy logic controller is implemented to control the traffic light system. In the proposed fuzzy logic controller, trapezoidal membership functions are combined with the rules to generate green light time for input QC and WT. The improvement in QC, WT, and tailback is witnessed by using Static Phase Scheduling Traffic Light System (SPSTLS) as a benchmark to measure the performance of the proposed controller. The performance comparison records a significant improvement of 81.68% in QC, 87.04% in average WT and 18.05% in the tailback. The results show that fuzzy logic controlled Dynamic Phase Scheduling Traffic Light System (DPSTLS) has the potential to resolve the problem of QC, WT, travel cost, accident, and traffic congestion.

Objective. In this paper, a novel methodology for feature extraction to enhance classification accuracy of functional near-infrared spectroscopy (fNIRS)-based two-class and three-class brain–computer interface (BCI) is presented. Approach. Novel features are extracted using vector-based phase analysis method. Changes in oxygenated Δ H b O and de-oxygenated ( Δ H b R ) haemoglobin are used to calculate four novel features: change in cerebral blood volume ( Δ C B V ), change in cerebral oxygen exchange ( Δ C O E ), vector magnitude (|L|) and angle (k). Δ C B V is the sum and Δ C O E is difference of Δ H b O and Δ H b R , whereas |L| is magnitude and k is angle of vector. fNIRS signals of seven healthy subjects, corresponding to left-hand index finger tapping (LFT), right-hand index finger tapping (RFT) and rest are acquired from motor cortex using multi-channel continuous-wave imaging system. After removing physiological and instrumental noises from the acquired signals, the four novel features are calculated. For validation, conventional temporal, spatial and spatiotemporal features; mean, peak, slope, variance, kurtosis and skewness are also calculated using Δ H b O and Δ H b R . All possible two-feature and three-feature combinations of the novel and conventional features are then used to classify two-class (LFT vs RFT) and three-class (LFT vs RFT vs rest) fNIRS-BCI using linear discriminant analysis. Main results. Results demonstrate that combination of four novel features yields significantly higher average classification accuracies of 98.7 ± 1.0% and 85.4 ± 1.4% as compared to 68.7 ± 6.9% and 53.6 ± 10.6% using conventional features for two-class and three-class problem, respectively. Validation of proposed method on an open access database containing RFT, LFT and dominant side foot tapping tasks for 30 subjects also shows improvement in average classification accuracies for two-class and three-class fNIRS-BCIs. Significance. This study provides a step forward in improving the classification accuracies of state-of-the-art fNIRS-BCIs by showing significant improvement in classification accuracies of two-class and three-class fNIRS-BCIs using novel features extracted by vector-based phase analysis.

Background In this paper, a novel functional near-infrared spectroscopy (fNIRS)-based brain-computer interface (BCI) framework for control of prosthetic legs and rehabilitation of patients suffering from locomotive disorders is presented. Methods fNIRS signals are used to initiate and stop the gait cycle, while a nonlinear proportional derivative computed torque controller (PD-CTC) with gravity compensation is used to control the torques of hip and knee joints for minimization of position error. In the present study, the brain signals of walking intention and rest tasks were acquired from the left hemisphere’s primary motor cortex for nine subjects. Thereafter, for removal of motion artifacts and physiological noises, the performances of six different filters (i.e. Kalman, Wiener, Gaussian, hemodynamic response filter (hrf), Band-pass, finite impulse response) were evaluated. Then, six different features were extracted from oxygenated hemoglobin signals, and their different combinations were used for classification. Also, the classification performances of five different classifiers (i.e. k-Nearest Neighbour, quadratic discriminant analysis, linear discriminant analysis (LDA), Naïve Bayes, support vector machine (SVM)) were tested. Results The classification accuracies obtained from SVM using the hrf were significantly higher (p

In this paper, a novel methodology for enhanced classification of functional near-infrared spectroscopy (fNIRS) signals utilizable in a two-class (motor imagery and rest; mental rotation and rest) brain-computer interface (BCI) is presented. First, fNIRS signals corresponding to motor imagery and mental rotation are acquired from the motor and prefrontal cortex respectively afterwards, filtered to remove physiological noises. Then, the signals are modelled using the general linear model (GLM), the coefficients of which are adaptively estimated using the least squares technique. Subsequently, multiple feature combinations of estimated coefficients were used for classification. The best classification accuracies achieved for five subjects, for motor imagery versus rest are 79.5, 83.7, 82.6, 81.4, and 84.1% whereas those for mental rotation versus rest are 85.5, 85.2, 87.8, 83.7, and 84.8%, respectively, using support vector machine (SVM). These results are compared with the best classification accuracies obtained using the conventional hemodynamic response. By means of the proposed methodology, the average classification accuracy obtained was significantly higher (p < 0.05). These results serve to demonstrate the feasibility of developing a high-classification-performance fNIRS-BCI.

In this paper, a novel technique for determination of the optimal feature combinations and, thereby, acquisition of the maximum classification performance for a functional near-infrared spectroscopy (fNIRS)-based brain-computer interface (BCI), is proposed. After obtaining motor-imagery and rest signals from the motor cortex, filtering is applied to remove the physiological noises. Six features (signal slope, signal mean, signal variance, signal peak, signal kurtosis and signal skewness) are then extracted from the oxygenated hemoglobin (HbO). Afterwards, the hybrid genetic algorithm (GA)-support vector machine (SVM) is applied in order to determine and classify 2- and 3-feature combinations across all subjects. The SVM classifier is applied to classify motor imagery versus rest. Moreover, four time windows (0-20s, 0-10s, 11-20s and 6-15s) are selected, and the hybrid GA-SVM is applied in order to extract the optimal 2- and 3-feature combinations. In the present study, the 11-20s time window showed significantly higher classification accuracies - the minimum accuracy was 91% - than did the other time windows (p

One of the pivotal issues which must be tackled when an effective brain-computer interface (BCI) is to be designed, is to reduce the enormous space of features extracted from fNIRS signals. BCI researchers often use genetic algorithms (GA) as the technique to extract features. The classic genetic algorithm obtains a feature set with the high classification accuracy; however, it is unable to create a reliable classifier due to the large dimensionality of this set. Herein, we propose a refined version of the genetic algorithm, which chooses a small number of features and determines the optimal feature combination for classification of fNIRS signals for a BCI system.

In this paper, we have compared and analyzed the classification accuracies using two different classifiers for mental arithmetic and rest task using functional near-infrared spectroscopy (fNIRS) signals. Multi-channel continuous-wave imaging system was used to extract the signals from the prefrontal cortex of the brain of seven healthy subjects. The extracted signals were then converted into change in concentration of oxygenated and deoxygenated hemoglobin using modified Beer-Lambert Law. Three features were extracted from the signals after removing the physiological noises. The classification accuracies using 2-feature combinations (signal variance and signal skewness; signal slope and signal variance; signal skewness and signal slope) for k-nearest neighbour (kNN) classifier are 93.8, 50.7 and 50.7% respectively and for artificial neural network (ANN) classifier are 97.9, 89.9, and 97.8% respectively.

We analyse and compare the classification accuracies of six different classifiers for a two-class mental task (mental arithmetic and rest) using functional near-infrared spectroscopy (fNIRS) signals. The signals of the mental arithmetic and rest tasks from the prefrontal cortex region of the brain for seven healthy subjects were acquired using a multichannel continuous-wave imaging system. After removal of the physiological noises, six features were extracted from the oxygenated hemoglobin (HbO) signals. Two- and three-dimensional combinations of those features were used for classification of mental tasks. In the classification, six different modalities, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA),k-nearest neighbour (kNN), the Naïve Bayes approach, support vector machine (SVM), and artificial neural networks (ANN), were utilized. With these classifiers, the average classification accuracies among the seven subjects for the 2- and 3-dimensional combinations of features were 71.6, 90.0, 69.7, 89.8, 89.5, and 91.4% and 79.6, 95.2, 64.5, 94.8, 95.2, and 96.3%, respectively. ANN showed the maximum classification accuracies: 91.4 and 96.3%. In order to validate the results, a statistical significance test was performed, which confirmed that thepvalues were statistically significant relative to all of the other classifiers (p< 0.005) using HbO signals.

The capabilities of Wireless Sensor Network (WSN) are unveiling with each passing day. Extensive use of WSNs is giving rise to different types of threats. To defend against the threats proper security schemes are required. Limited area, power and memory of WSNs implement strict constraints on the selection of cryptographic techniques. Elliptic Curve Cryptography (ECC) is the best candidate due to its smaller key size. High security despite of smaller key size results in area and power efficient crypto systems. This paper describes the hardware implementation of Elliptic Curve based asymmetric cryptosystem for wireless sensor networks. The field used for elliptic curve is defined over prime number. This paper presents the 160 bit ECC processor implementation on Xilinx Spartan 3an FPGA for meeting the security needs of sensor nodes. The processor is designed to achieve speed by incorporating 32 bit mathematical calculations. The design also provides excellent area results.