Classification Of Tasks Research Articles

Large-Scale Cortical Network Analysis and Classification of MI-BCI Tasks Based on Bayesian Nonnegative Matrix Factorization.

Motor imagery (MI) is a high-level cognitive process that has been widely applied to clinical rehabilitation and brain-computer interfaces (BCIs). However, the decoding of MI tasks still faces challenges, and the neural mechanisms underlying its application are unclear, which seriously hinders the development of MI-based clinical applications and BCIs. Here, we combined EEG source reconstruction and Bayesian nonnegative matrix factorization (NMF) methods to construct large-scale cortical networks of left-hand and right-hand MI tasks. Compared to right-hand MI, the results showed that the significantly increased functional network connectivities (FNCs) mainly located among the visual network (VN), sensorimotor network (SMN), right temporal network, right central executive network, and right parietal network in the left-hand MI at the β (13-30Hz) and all (8-30Hz) frequency bands. For the network properties analysis, we found that the clustering coefficient, global efficiency, and local efficiency were significantly increased and characteristic path length was significantly decreased in left-hand MI compared to right-hand MI at the β and all frequency bands. These network pattern differences indicated that the left-hand MI may need more modulation of multiple large-scale networks (i.e., VN and SMN) mainly located in the right hemisphere. Finally, based on the spatial pattern network of FNC and network properties, we propose a classification model. The proposed model achieves a top classification accuracy of 78.2% in cross-subject two-class MI-BCI tasks. Overall, our findings provide new insights into the neural mechanisms of MI and a potential network biomarker to identify MI-BCI tasks.

AI-Based Task Classification With Pressure Insoles for Occupational Safety

AI-Based Task Classification With Pressure Insoles for Occupational Safety

Moving Beyond the Plateau with Computer-Assisted Pronunciation Mediation

This paper examines the plateau effect and the role of Computer-Assisted Pronunciation Training (CAPT) in enabling learners to move beyond the plateau. Specifically, the samples and empirical evidence for this study is provided by StudyIntonation, a top-down CAPT environment supporting visual display of phrasal intonation in the form of pitch graphs presented for both the model pitch of native speakers and attempts of learners. Utterances of model speakers are grouped by situational context, prosodic forms and mode of feedback. The system largely relies upon concepts of sociocultural theory, such as mediation through the zone of proximal development. With the help of an experimental group of high school students, we use mathematical models to pinpoint the Zone of Proximal Development (ZPD), allowing for delivery of tailored task classification based on syntax and discourse tags. Longitudinal pronunciation assessment supported by dynamic time warping and cross-recurrence quantification analysis is used to locate the ZPD, and provide personalized practice. Preliminary experiments for the current study demonstrate that this approach can help learners in overcoming the plateau effect experienced during the learning process. The reported new findings create further grounds for improving and targeting the CAPT system feedback to learners with respect to their individual achievements and differences in learning styles.

Mitigating Bias in Aesthetic Quality Control Tasks: An Adversarial Learning Approach

Aesthetic quality control (AQC) is an essential step in smart factories to ensure that product quality meets the desired standards. This operation includes assessing factors such as color, texture, and shape. In the context of AQC, bias can arise when the criteria used to evaluate the aesthetics of a product are subjective and influenced by personal preferences. Bias can also occur due to the background or other objective factors like the geometry of the material. This work will focus on applying an adversarial learning strategy to a pre-trained DL architecture for improving the generalization performance of a predictive model tailored explicitly for solving AQC task classification. Experimental results on a benchmark AQC dataset highlighted the robustness of the proposed methodology for learning only relevant components related to quality classes rather than other confusing traits, enabling the mitigation of the identified bias.

Discourse on the Classification of Information in Nepal

Even after one and a half decades since the implementation of the Right to Information (RTI) Act in Nepal, the classification of information tasks has not been completed. Although attempts were made to classify the information three times during this period, none of the classifications were valid, and there was difficulty in the implementation of the RTI Act. This study has employed the principle of maximum disclosure, and the contents have been analyzed by adopting descriptive and explanatory methods. Instead of being active by classifying information to fulfill the policy objectives of the RTI Act, such as making the functions of the state open and transparent in accordance with the democratic system, making it accountable and responsible to the citizens, and making the access of citizens simple and easy to the information of public importance held in public bodies, the state has become indifferent.

A model for identifying project sprint tasks based on their description

The subject of research in this article is the identification of project sprint tasks. The purpose of the article is to find approaches to reducing the risks of not fulfilling sprint tasks. The article solves the following tasks: analyzing research on the classification and visualization of project tasks, developing an algorithm that can automatically classify text descriptions of sprint tasks, collecting and preparing a training sample of text descriptions of sprint tasks for training and testing the classification model, applying natural language processing methods to improve classification and ensure the accuracy of the results, validating the model on real data to assess the efficiency and accuracy of classification, and analyzing the results. The following methods have been used: machine learning methods for classification, text vectorization methods, methods for classifying text descriptions, natural language processing methods, methods for semantic analysis of task description text, methods for processing expert opinions. The following results were obtained: a comprehensive approach to using machine learning algorithms, including the collection and processing of textual descriptions of tasks, for classification and involvement of expert opinions to improve the quality of task perception by the project team. Text expressions were classified based on the Bayesian classifier and neural classifiers. A visual representation of the data was implemented. Semantic analysis of the text of the description and title of the tasks was performed. Data markup was obtained to classify the quality of the wording, which was performed by a team of experts. To measure the reliability of the obtained expert assessments, we calculated Cohen's kappa coefficient for each pair of markers. According to the experimental results, the accuracy of the Bayesian classifier is 70%. For the classifier based on deep learning, a neural network for binary classification based on the transformer architecture was selected. The neural network was trained using the Python programming language and deep learning frameworks. The result is a classifier that gives an accuracy score of 83% on a test dataset, which is a good result for a small dataset and data with conflicting labels. Conclusions: The analysis of textual data confirms that the existing data in the tracking system is incomplete and contains abbreviations, conventions, and slang. The results show that the assessment of the quality of the wording is determined by the level of expert knowledge of the specifics and context of the project, while increasing the number of experts has almost no effect on the result. In further research, it is recommended to test the hypothesis that the effectiveness of the classifier depends on the specific project and the use of unsupervised learning methods for the task of identifying the quality of formulations.

Fusion of Deep Features from 2D-DOST of fNIRS Signals for Subject-Independent Classification of Motor Execution Tasks

Functional near-infrared spectroscopy (fNIRS) is a low-cost and noninvasive method to measure the hemodynamic responses of cortical brain activities and has received great attention in brain-computer interface (BCI) applications. In this paper, we present a method based on deep learning and the time-frequency map (TFM) of fNIRS signals to classify the three motor execution tasks including right-hand tapping, left-hand tapping, and foot tapping. To simultaneously obtain the TFM and consider the correlation among channels, we propose to utilize the two-dimensional discrete orthonormal Stockwell transform (2D-DOST). The TFMs for oxygenated hemoglobin (HbO), reduced hemoglobin (HbR), and two linear combinations of them are obtained and then we propose three fusion schemes for combining their deep information extracted by the convolutional neural network (CNN). Two CNNs, LeNet and MobileNet, are considered and their structures are modified to maximize the accuracy. Due to the lack of enough signals for training CNNs, data augmentation based on the Wasserstein generative adversarial network (WGAN) is performed. Several simulations are performed to assess the performance of the proposed method in three-class and binary scenarios. The results present the efficiency of the proposed method in different scenarios. Also, the proposed method outperforms the recently introduced methods.

An improved human activity recognition technique based on convolutional neural network

A convolutional neural network (CNN) is an important and widely utilized part of the artificial neural network (ANN) for computer vision, mostly used in the pattern recognition system. The most important applications of CNN are medical image analysis, image classification, object recognition from videos, recommender systems, financial time series analysis, natural language processing, and human–computer interfaces. However, after the technological advancement in the power of computing ability and the emergence of huge quantities of labeled data provided through enhanced algorithms, nowadays, CNN is widely used in almost every area of study. One of the main uses of wearable technology and CNN within medical surveillance is human activity recognition (HAR), which must require constant tracking of everyday activities. This paper provides a comprehensive study of the application of CNNs in the classification of HAR tasks. We describe their enhancement, from their antecedents up to the current state-of-the-art systems of deep learning (DL). We have provided a comprehensive working principle of CNN for HAR tasks, and a CNN-based model is presented to perform the classification of human activities. The proposed technique interprets data from sensor sequences of inputs by using a multi-layered CNN that gathers temporal and spatial data related to human activities. The publicly available WISDM dataset for HAR has been used to perform this study. This proposed study uses the two-dimensional CNN approach to make a model for the classification of different human activities. A recent version of Python software has been used to perform the study. The rate of accuracy for HAR through the proposed model in this experiment is 97.20%, which is better than the previously estimated state-of-the-art technique. The findings of the study imply that using DL methods for activity recognition might greatly increase accuracy and increase the range of applications where HAR can be used successfully. We have also described the future research trends in the field of HAR in this article.

Развиване на здравно-екологични компетентности в обучението по химия чрез система от учебни задачи

The choice of the teaching tasks as the main didactic tool for developing of health-ecological competences in chemistry education is reasoned. Criteria for the classification of chemistry tasks with health-ecological orientation, which can be used in the composition and selection of such tasks, are derived. On the basis of the proposed criteria, a classification of chemistry teaching tasks with health-ecological content is created. It is the basis of the developed system of chemistry tasks in the learning of specific educational content in view of forming health-ecological competencies in students. The appropriateness of the composed teaching tasks with health-ecological content is proved by pedagogical experiment. Its results show that the designed didactic tools succeed in achieving the goals of forming health-ecological knowledge, skills and value attitudes in adolescents.

A Review of Studies on Task-based Language Teaching

In the 1980s, a new teaching method - Task-based Language Teaching - gradually emerged in the field of Western education. Task-based Language Teaching is a practical teaching method that involves specific communication and language tasks throughout the teaching process. It has specific content and is easy to operate, making it a very popular educational method in the current education circles. This article is a review paper that summarizes the definitions and classifications of tasks in Task-based Language Teaching by reviewing different well-known scholars previous research on Task-based Language Teaching. It lists two models proposed by Willis and Ellis for implementing Task-based Language Teaching. This article also takes three predecessors research on the application of this teaching method in actual English classrooms as an example to illustrate its effectiveness in a practical environment. The article also analyzes the advantages and disadvantages of Task-based Language Teaching, and summarizes how teachers can design tasks to avoid various drawbacks of Task-based Language Teaching and improve teaching efficiency when using it.

COMMUNICATION ORIENTED APPROACH TO THE FORMATION OF GRAMMAR COMPETENCE IN THE STUDENTS OF HIGHER MEDICAL EDUCATIONAL INSTITUTIONS

The article deals with grounding of the use of English grammar tasks as a component of forming communicative competence in the students of higher medical educational institutions. The essence of grammar competence as an important component of the future medic's communicative competence is found out, and the need for learning grammar in order to be able to express one’s thoughts properly is emphasized. English communicative approach to medical students’ professional competence is an integral aspect of professionally oriented study at the higher medical educational institution, and is formed through mastering the specifics of language competence at its phonetic, lexical, and grammar levels, particular attention being focused on the analysis of scientifically grounded classification of the exercise system aimed at the formation of grammar competence as a fundamental basis for professionally targeted English course. The issues of semantic content for professionally targeted English, selection of training grammatical material, and development of communicative component in the process of students' mastering English language skills are under consideration. Main components of medical students' grammar competence are characterized; science-based classification of grammar tasks for the formation of English language communication is considered, the emphasis being put on the role of grammar for the proper formulation of thoughts. Both theoretical and methodological aspects of communicative approach to learning English grammar, which is studied through combining grammar, lexical, and phonetic phenomena, are covered. In particular, when teaching correct sentence construction, their semantic and thematic content are considered, grammar skills being formed both at the level of separate structures and fluent dialogue-monologue speech. The use of communicative type grammar tasks at professionally targeted classes of English has been found to develop speech skills and abilities that make possible to estimate and express one's thoughts along with training attention, memory, developing thinking and having high motivation characteristics.

Orientation tasks in the system of pedagogical stimulation of forming a university student learning strategy

The article is devoted to the problem of forming a student learning strategy. The urgency of the problem is justified by the necessity to meet the need of modern society in people who are capable and striving for continuous education and self-realization. The authors consider the concept of a student learning strategy as an important phenomenon necessary to achieve the goal of learning, successfully mastering not only knowledge, skills and abilities, but also their application in various types of activity. Attention is focused on the development of ways to implement educational actions that contribute to the design of the upcoming learning strategy, its implementation, monitoring of one’s own educational achievements and the gradual mastery of the technology of its independent development and implementation in the learning process. The components of the learning strategy (incentive and oriented, operational and technological, analytical and reflective) are highlighted. The Incentive and orientation component includes the need to achieve the learning goal, the dominant motives and values of the student. The operational and technological component is represented by the formation of predictive skills and the ability to regulate the process of building and implementing a learning strategy. The analytical and reflexive component consists of the ability to analyze and introspect the changes taking place in the process of forming a learning strategy. Orientation tasks act as one of the means of forming a student learning strategy. The authors propose a classification of orientation tasks into aspect and integration tasks. Aspect orientation tasks are aimed at updating, developing and correcting the highlighted components of the learning strategy. Integration orientation tasks are aimed at implementing a learning strategy by combining all the available experience and resources of the student. The article provides examples of the application of orientation tasks. The developed orientation tasks can be used both in classes in various disciplines and in extracurricular activity (research, talks, trainings). The authors have established that orientation tasks developed and implemented in the educational process of the university are an effective tool that contributes to a well-founded and high-quality pedagogical stimulation of the formation and implementation of learning strategy by a student.

Predicting anticancer synergistic drug combinations based on multi-task learning

BackgroundThe discovery of anticancer drug combinations is a crucial work of anticancer treatment. In recent years, pre-screening drug combinations with synergistic effects in a large-scale search space adopting computational methods, especially deep learning methods, is increasingly popular with researchers. Although achievements have been made to predict anticancer synergistic drug combinations based on deep learning, the application of multi-task learning in this field is relatively rare. The successful practice of multi-task learning in various fields shows that it can effectively learn multiple tasks jointly and improve the performance of all the tasks.MethodsIn this paper, we propose MTLSynergy which is based on multi-task learning and deep neural networks to predict synergistic anticancer drug combinations. It simultaneously learns two crucial prediction tasks in anticancer treatment, which are synergy prediction of drug combinations and sensitivity prediction of monotherapy. And MTLSynergy integrates the classification and regression of prediction tasks into the same model. Moreover, autoencoders are employed to reduce the dimensions of input features.ResultsCompared with the previous methods listed in this paper, MTLSynergy achieves the lowest mean square error of 216.47 and the highest Pearson correlation coefficient of 0.76 on the drug synergy prediction task. On the corresponding classification task, the area under the receiver operator characteristics curve and the area under the precision–recall curve are 0.90 and 0.62, respectively, which are equivalent to the comparison methods. Through the ablation study, we verify that multi-task learning and autoencoder both have a positive effect on prediction performance. In addition, the prediction results of MTLSynergy in many cases are also consistent with previous studies.ConclusionOur study suggests that multi-task learning is significantly beneficial for both drug synergy prediction and monotherapy sensitivity prediction when combining these two tasks into one model. The ability of MTLSynergy to discover new anticancer synergistic drug combinations noteworthily outperforms other state-of-the-art methods. MTLSynergy promises to be a powerful tool to pre-screen anticancer synergistic drug combinations.

Lower limb rehabilitation exoskeleton using brain–computer interface based on multiband filtering with classifier fusion

AbstractIn this study, we propose two adaptive frameworks based on evolutionary algorithms for improving the accuracy of motor imagery (MI) task classification. In an MI task, each participant has a different reaction time delay. In this study, the reaction delay was used as a parameter. Specifically, the time point after the reaction delay was defined as the starting point (SP) when MI began. Various classification and optimization algorithms were employed. The methods include the use of a fixed and dynamic SP for each individual, a genetic algorithm, particle swarm optimization (PSO), spatial filter tuning, the use of common spatial patterns, linear discriminant analysis, bagged decision trees, convolutional neural network (CNN), and long short‐term memory (LSTM). Moreover, a dynamic optimization method for multiband filtering (MBF) method for the subbands of EEG signals was developed that improved accuracy by 5.4%. Combining MBF with bagged decision trees with PSO‐optimized parameters achieved the highest accuracy of 82.3%. Moreover, after optimization by PSO, the CNN‐LSTM method achieved a classification accuracy of 81.4% for 1‐s signals, shorter than those in other studies. The method enables the use of EEG signals to control an exoskeleton for patient rehabilitation.

ASAP-CORPS: A Semi-Autonomous Platform for COntact-Rich Precision Surgery.

Remote military operations require rapid response times for effective relief and critical care. Yet, the military theater is under austere conditions, so communication links are unreliable and subject to physical and virtual attacks and degradation at unpredictable times. Immediate medical care at these austere locations requires semi-autonomous teleoperated systems, which enable the completion of medical procedures even under interrupted networks while isolating the medics from the dangers of the battlefield. However, to achieve autonomy for complex surgical and critical care procedures, robots require extensive programming or massive libraries of surgical skill demonstrations to learn effective policies using machine learning algorithms. Although such datasets are achievable for simple tasks, providing a large number of demonstrations for surgical maneuvers is not practical. This article presents a method for learning from demonstration, combining knowledge from demonstrations to eliminate reward shaping in reinforcement learning (RL). In addition to reducing the data required for training, the self-supervised nature of RL, in conjunction with expert knowledge-driven rewards, produces more generalizable policies tolerant to dynamic environment changes. A multimodal representation for interaction enables learning complex contact-rich surgical maneuvers. The effectiveness of the approach is shown using the cricothyroidotomy task, as it is a standard procedure seen in critical care to open the airway. In addition, we also provide a method for segmenting the teleoperator's demonstration into subtasks and classifying the subtasks using sequence modeling. A database of demonstrations for the cricothyroidotomy task was collected, comprising six fundamental maneuvers referred to as surgemes. The dataset was collected by teleoperating a collaborative robotic platform-SuperBaxter, with modified surgical grippers. Then, two learning models are developed for processing the dataset-one for automatic segmentation of the task demonstrations into a sequence of surgemes and the second for classifying each segment into labeled surgemes. Finally, a multimodal off-policy RL with rewards learned from demonstrations was developed to learn the surgeme execution from these demonstrations. The task segmentation model has an accuracy of 98.2%. The surgeme classification model using the proposed interaction features achieved a classification accuracy of 96.25% averaged across all surgemes compared to 87.08% without these features and 85.4% using a support vector machine classifier. Finally, the robot execution achieved a task success rate of 93.5% compared to baselines of behavioral cloning (78.3%) and a twin-delayed deep deterministic policy gradient with shaped rewards (82.6%). Results indicate that the proposed interaction features for the segmentation and classification of surgical tasks improve classification accuracy. The proposed method for learning surgemes from demonstrations exceeds popular methods for skill learning. The effectiveness of the proposed approach demonstrates the potential for future remote telemedicine on battlefields.

Hybrid optimization assisted channel selection of EEG for deep learning model-based classification of motor imagery task.

To design and develop an approach named HC+SMA-SSA scheme for classifying motor imagery task. The offered model employs a new method for classifying motor imagery task. Initially, down sampling is deployed to pre-process the incoming signal. Subsequently, "Modified Stockwell Transform (ST) and common spatial pattern (CSP) based features are extracted". Then, optimal channel selection is made by a novel hybrid optimization model named as Spider Monkey Assisted SSA (SMA-SSA). Here, "Long Short Term Memory (LSTM) and Bidirectional Gated Recurrent Unit (BI-GRU)" models are used for final classification, whose outcomes are averaged at the end. At last, the improvement of SMA-SSA based model is proven over different metrics. A superior sensitivity of 0.939 is noted for HC+SMA-SSA that was higher over HC with no optimization and proposed with traditional ST. The proposed method achieved effective classification performance in terms of performance measures.

Region-aware dynamic job scheduling and resource efficiency for load balancing based on adaptive chaotic sparrow search optimization and coalitional game in cloud computing environments

Uneven distribution of workloads represents a significant challenge in cloud data centers, negatively impacting the efficient utilization of resources. To address this problem and optimize the performance-to-profit (PP) ratio, we introduced the Regional Awareness dynamic Scheduling Algorithm (RASA) through a three-phase approach. In the initial phase, tasks are categorized based on critical parameters like CPU usage, memory utilization, and I/O operations, using a task classification model. The second phase involves estimating the CPU, memory, and I/O capacities of each node. Subsequently, a unique merge-and-split-based coalitional game-theoretic process is employed to group nodes into clusters. In the final phase, we have improved task placement by introducing an enhanced Sparrow Search Algorithm (ISSA) to overcome the slow convergence and local optimization issues found in the traditional SSA. We have incorporated the Tent chaotic mapping approach to enhance both global and local search efficiency during this stage. Additionally, we have fine-tuned fair load distribution using the Cauchy mutation process. We performed simulations using the CloudSim toolkit, and the results underscore the RASA algorithm’s superiority in terms of how quickly it converges its precision in task placement and its ability to distribute workloads fairly. Our proposed algorithm achieves a 9% reduction in latency overhead, a 14% decrease in processing time, a 15% reduction in workload imbalance, a 19% decrease in energy consumption, and a 26% decrease in idle periods. Furthermore, it enhances resource availability and efficiency by 22% and 27% respectively, while simultaneously boosting service throughput by 32%.

Task Classification and Scheduling Using Enhanced Coot Optimization in Cloud Computing

Task Classification and Scheduling Using Enhanced Coot Optimization in Cloud Computing

Tri-model classifiers for EEG based mental task classification: hybrid optimization assisted framework

The commercial adoption of BCI technologies for both clinical and non-clinical applications is drawing scientists to the creation of wearable devices for daily living. Emotions are essential to human existence and have a significant impact on thinking. Emotion is frequently linked to rational decision-making, perception, interpersonal interaction, and even basic human intellect. The requirement for trustworthy and implementable methods for the detection of individual emotional responses is needed with rising attention of the scientific community towards the establishment of some significant emotional connections among people and computers. This work introduces EEG recognition model, where the input signal is pre-processed using band pass filter. Then, the features like discrete wavelet transform (DWT), band power, spectral flatness, and improved Entropy are extracted. Further, for recognition, tri-classifiers like long short term memory (LSTM), improved deep belief network (DBN) and recurrent neural network (RNN) are used. Also to enhance tri-model classifier performance, the weights of LSTM, improved DBN, and RNN are tuned by model named as shark smell updated BES optimization (SSU-BES). Finally, the perfection of SSU-BES is demonstrated over diverse metrics.

High-Density Functional Near-Infrared Spectroscopy and Machine Learning for Visual Perception Quantification.

The main application scenario for wearable sensors involves the generation of data and monitoring metrics. fNIRS (functional near-infrared spectroscopy) allows the nonintrusive monitoring of human visual perception. The quantification of visual perception by fNIRS facilitates applications in engineering-related fields. This study designed a set of experimental procedures to effectively induce visible alterations and to quantify visual perception in conjunction with the acquisition of Hbt (total hemoglobin), Hb (hemoglobin), and HbO2 (oxygenated hemoglobin) data obtained from HfNIRS (high-density functional near-infrared spectroscopy). Volunteers completed the visual task separately in response to different visible changes in the simulated scene. HfNIRS recorded the changes in Hbt, Hb, and HbO2 during the study, the time point of the visual difference, and the time point of the task change. This study consisted of one simulated scene, two visual variations, and four visual tasks. The simulation scene featured a car driving location. The visible change suggested that the brightness and saturation of the car operator interface would change. The visual task represented the completion of the layout, color, design, and information questions answered in response to the visible change. This study collected data from 29 volunteers. The volunteers completed the visual task separately in response to different visual changes in the same simulated scene. HfNIRS recorded the changes in Hbt, Hb, and HbO2 during the study, the time point of the visible difference, and the time point of the task change. The data analysis methods in this study comprised a combination of channel dimensionality reduction, feature extraction, task classification, and score correlation. Channel downscaling: This study used the data of 15 channels in HfNIRS to calculate the mutual information between different channels to set a threshold, and to retain the data of the channels that were higher than those of the mutual information. Feature extraction: The statistics derived from the visual task, including time, mean, median, variance, extreme variance, kurtosis, bias, information entropy, and approximate entropy were computed. Task classification: This study used the KNN (K-Nearest Neighbors) algorithm to classify different visual tasks and to calculate the accuracy, precision, recall, and F1 scores. Scoring correlation: This study matched the visual task scores with the fluctuations of Hbt, Hb, and HbO2 and observed the changes in Hbt, Hb, and HbO2 under different scoring levels. Mutual information was used to downscale the channels, and seven channels were retained for analysis under each visual task. The average accuracy was 96.3% ± 1.99%; the samples that correctly classified the visual task accounted for 96.3% of the total; and the classification accuracy was high. By analyzing the correlation between the scores on different visual tasks and the fluctuations of Hbt, Hb, and HbO2, it was found that the higher the score, the more obvious, significant, and higher the fluctuations of Hbt, Hb, and HbO2. Experiments found that changes in visual perception triggered changes in Hbt, Hb, and HbO2. HfNIRS combined with Hbt, Hb, and HbO2 recorded by machine learning algorithms can effectively quantify visual perception. However, the related research in this paper still needs to be further refined, and the mathematical relationship between HfNIRS and visual perception needs to be further explored to realize the quantitative study of subjective and objective visual perception supported by the mathematical relationship.