Intention Detection Research Articles

MCR-ALS-based muscle synergy extraction method combined with LSTM neural network for motion intention detection.

The time-varying and individual variability of surface electromyographic signals (sEMG) can lead to poorer motor intention detection results from different subjects and longer temporal intervals between training and testing datasets. The consistency of using muscle synergy between the same tasks may be beneficial to improve the detection accuracy over long time ranges. However, the conventional muscle synergy extraction methods, such as non-negative matrix factorization (NMF) and principal component analysis (PCA) have some limitations in the field of motor intention detection, especially in the continuous estimation of upper limb joint angles. In this study, we proposed a reliable multivariate curve-resolved-alternating least squares (MCR-ALS) muscle synergy extraction method combined with long-short term memory neural network (LSTM) to estimate continuous elbow joint motion by using the sEMG datasets from different subjects and different days. The pre-processed sEMG signals were then decomposed into muscle synergies by MCR-ALS, NMF and PCA methods, and the decomposed muscle activation matrices were used as sEMG features. The sEMG features and elbow joint angular signals were input to LSTM to establish a neural network model. Finally, the established neural network models were tested by using sEMG dataset from different subjects and different days, and the detection accuracy was measured by correlation coefficient. The detection accuracy of elbow joint angle was more than 85% by using the proposed method. This result was significantly higher than the detection accuracies obtained by using NMF and PCA methods. The results showed that the proposed method can improve the accuracy of motor intention detection results from different subjects and different acquisition timepoints. This study successfully improves the robustness of sEMG signals in neural network applications using an innovative muscle synergy extraction method. It contributes to the application of human physiological signals in human-machine interaction.

Research on Joint Modeling of Intent Detection and Slot Filling

Abstract In task-based dialogue system, the key of the natural language understanding module is intent detection and slot filling. At this stage, Joint modeling of intention detection and slot filling tasks has become the mainstream and achieved good results. In order to investigate the correlation between intention detection and slot filling tasks, Joint model of intention detection and slot filling based on attention mechanism in three dimensions: one-way modeling from intention to slot, Unidirectional modeling from slot to intention and bidirectional modeling from intention to slot Separately. And experiments were conducted using the Chinese dataset CAIS, and the results showed three evaluation results for time slot F1.The intention accuracy and overall accuracy of joint models for intention detection and filling gaps are usually higher than those of unidirectional models.

Lower Limb Exoskeleton for Rehabilitation with Flexible Joints and Movement Routines Commanded by Electromyography and Baropodometry Sensors.

This paper presents the development of an instrumented exoskeleton with baropodometry, electromyography, and torque sensors. The six degrees of freedom (Dof) exoskeleton has a human intention detection system based on a classifier of electromyographic signals coming from four sensors placed in the muscles of the lower extremity together with baropodometric signals from four resistive load sensors placed at the front and rear parts of both feet. In addition, the exoskeleton is instrumented with four flexible actuators coupled with torque sensors. The main objective of the paper was the development of a lower limb therapy exoskeleton, articulated at hip and knees to allow the performance of three types of motion depending on the detected user's intention: sitting to standing, standing to sitting, and standing to walking. In addition, the paper presents the development of a dynamical model and the implementation of a feedback control in the exoskeleton.

53. Evaluation of User Motor Intention Detection for Exoskeleton Control with the Muscle Cuff Regenerative Peripheral Nerve Interface (MC-RPNI) in a Rat Model

53. Evaluation of User Motor Intention Detection for Exoskeleton Control with the Muscle Cuff Regenerative Peripheral Nerve Interface (MC-RPNI) in a Rat Model

Joint Intent Detection Model for Task-oriented Human-Computer Dialogue System using Asynchronous Training

How to accurately understand low-resource languages is the core of the task-oriented human-computer dialogue system. Language understanding consists of two sub-tasks, i.e., intent detection and slot filling. Intent detection still faces challenges due to semantic ambiguity and implicit intentions with users’ input. Moreover, separately modeling intent detection and slot filling significantly decrease the correctness and relevance between questions and answers. To address these issues, we propose a joint intent detection method using asynchronous training strategy. The proposed method firstly encodes local text information extracted by CNN and relationship information among words emphasized by attention structure. Later, a joint intent detection model with asynchronous training strategy is proposed by either fusing hidden states of intent detection and slot filling layers, or adopting the key information to fine-tune the whole network, greatly increasing the relevance of intent detection and slot filling subtasks. The accuracy achieved by the proposed method tested on an open-source airline travel dataset and a self-collected electricity service dataset, i.e., ATIS and ECSF, are 97.49% and 89.68%, respectively, which proves the effectiveness of joint learning and asynchronous training.

Sitting and Standing Intention Detection Based on Dynamical Region Connectivity and Entropy of EEG

Based on the brain signals, decoding and analyzing the gait features to make a reliable prediction of action intention are the core issues in the brain computer interface (BCI)-based hybrid rehabilitation and intelligent walking aid robot system. In order to realize the classification and recognition of the most basic gait processes such as standing, sitting, and quiet, this paper proposes a feature representation method based on the signal complexity and entropy of each brain region. Through the statistical analysis of these parameters between different conditions, these characteristics which sensitive to different actions are determined as a feature vector, and the classification and recognition of these actions are completed by combing support vector machine, linear discriminant analysis, and logistic regression. Experimental results show the proposed method can better realize the recognition of the aforementioned action intention. The recognition accuracy of standing, sitting, and quiet of 13 subjects is higher than 80.9%, and the highest one can reach 86.8%. Directed dynamic brain network analysis of the 8 brain regions shows that the occurrence of lower limb movement will weaken the dependence between brain regions, resulting in the weakening of network topological connection. The result has significant value for understanding human’s brain cognitive characteristics in the process of lower limb movement and carrying out the study of BCI based strategy and system for lower limb rehabilitation.

MEASURING SUICIDAL IDEATION AMONG DRUG ADDICTS IN THE REHABILITATION CENTER

Abstract: The prevalence of suicide among drug addicts is common and one of the biggest challenges faced by society. Studies have found that substance use is associated with suicide ideation. Early detection of suicidal intent among addicts is important to ensure they do not take further action to prevent suicidal acts. This study used a psychometric instrument, namely the Suicidal Ideation Behavioural Assessment (SIBA®). The four main components involved were interpersonal conflict, emotional disorder, depression, and hopelessness. A descriptive study was conducted in this study. Altogether, a total of 123 drug addicts in Malaysia participated in this study as respondents. The descriptive analysis shows that 52.85% of addicts had low suicidal ideation behaviour, 21.95% were moderate, 3.25% high, and 0.81% very high. Findings from this study showed that drug addicts have high suicide ideation, which can be a risk factor for suicide attempts. It is suggested to focus on early detection of suicide ideation. This study is expected to make a significant contribution to the government, especially the rehabilitation centre, in helping addicts who have suicidal ideation behaviours to be given better attention and psychosocial support services.
 Keywords: Conflict Interpersonal; Emotional Disorder; Depression; Hopelessness; Suicidal Ideation Behavioural Assessment

Model predictive impedance control with Gaussian processes for human and environment interaction

Robotic tasks which involve uncertainty – due to variation in goal, environment configuration, or confidence in task model – may require human input to instruct or adapt the robot. In tasks with physical contact, several existing methods for adapting robot trajectory or impedance according to individual uncertainties have been proposed, e.g., realizing intention detection or uncertainty-aware learning from demonstration. However, isolated methods cannot address the wide range of uncertainties jointly present in many tasks.To improve generality, this paper proposes a model predictive control (MPC) framework which plans both trajectory and impedance online, can consider discrete and continuous uncertainties, includes safety constraints, and can be efficiently applied to a new task. This framework can consider uncertainty from: contact constraint variation, uncertainty in human goals, or task disturbances. An uncertainty-aware task model is learned from a few (≤3) demonstrations using Gaussian Processes. This task model is used in a nonlinear MPC problem to optimize robot trajectory and impedance according to belief in discrete human goals, human kinematics, safety constraints, contact stability, and frequency-domain disturbance rejection. This MPC formulation is introduced, analyzed with respect to convexity, and validated in co-manipulation with multiple goals, a collaborative polishing task, and a collaborative assembly task.

On the Use of Transformer-Based Models for Intent Detection Using Clustering Algorithms

Chatbots are becoming increasingly popular and require the ability to interpret natural language to provide clear communication with humans. To achieve this, intent detection is crucial. However, current applications typically need a significant amount of annotated data, which is time-consuming and expensive to acquire. This article assesses the effectiveness of different text representations for annotating unlabeled dialog data through a pipeline that examines both classical approaches and pre-trained transformer models for word embedding. The resulting embeddings were then used to create sentence embeddings through pooling, followed by dimensionality reduction, before being fed into a clustering algorithm to determine the user’s intents. Therefore, various pooling, dimension reduction, and clustering algorithms were evaluated to determine the most appropriate approach. The evaluation dataset contains a variety of user intents across different domains, with varying intent taxonomies within the same domain. Results demonstrate that transformer-based models perform better text representation than classical approaches. However, combining several clustering algorithms and embeddings from dissimilar origins through ensemble clustering considerably improves the final clustering solution. Additionally, applying the uniform manifold approximation and projection algorithm for dimension reduction can substantially improve performance (up to 20%) while using a much smaller representation.

Improving NLP Accuracy with Stack-Propagation and Knowledge Distillation: A Joint Model for Intent Detection and Slot Filling

Intent detection and slot filling are fundamental tasks in Natural Language Understanding (NLU), constituting important components of intelligent question-answering systems. These tasks are closely interrelated, forming a core aspect of semantic understanding in natural language processing. In this paper, we propose an architecture using Stack-Propagation to improve the accuracy of NLU tasks. In stack propagation, we use a joint model that mainly incorporates token-level intent detection data with sentence word vectors as input for slot filling to capture intent semantic knowledge. Additionally, we use knowledge distillation (KD) to improve model efficiency and enhance the correlation between the two tasks. Our proposed framework significantly differs from existing joint models as it directly leverages intent information in the joint model and adopts token-level intent information for slot filling to ease error propagation. Furthermore, our model can explicitly incorporate intent information for slot filling with Stack-Propagation, making the interaction procedure more interpretable, while other models only interact with hidden states implicitly between the two tasks. We experimentally evaluated our model on two publicly available datasets, and the results demonstrate that it achieves state-of-the-art performance and outperforms previous methods by a significant margin, indicating its superiority in addressing the slot-filling and intent detection tasks. In future research, we will combine the stack-propagation frame with the KD module in the Transform model, which can boost our model performance to move forward a single step in the NLU task.

Explaining Recommendations through Conversations: Dialog Model and the Effects of Interface Type and Degree of Interactivity

Explaining system-generated recommendations based on user reviews can foster users’ understanding and assessment of the recommended items and the recommender system (RS) as a whole. While up to now explanations have mostly been static, shown in a single presentation unit, some interactive explanatory approaches have emerged in explainable artificial intelligence (XAI), making it easier for users to examine system decisions and to explore arguments according to their information needs. However, little is known about how interactive interfaces should be conceptualized and designed to meet the explanatory aims of transparency, effectiveness, and trust in RS. Thus, we investigate the potential of interactive, conversational explanations in review-based RS and propose an explanation approach inspired by dialog models and formal argument structures. In particular, we investigate users’ perception of two different interface types for presenting explanations, a graphical user interface (GUI)-based dialog consisting of a sequence of explanatory steps, and a chatbot-like natural-language interface. Since providing explanations by means of natural language conversation is a novel approach, there is a lack of understanding how users would formulate their questions with a corresponding lack of datasets. We thus propose an intent model for explanatory queries and describe the development of ConvEx-DS, a dataset containing intent annotations of 1,806 user questions in the domain of hotels, that can be used to to train intent detection methods as part of the development of conversational agents for explainable RS. We validate the model by measuring user-perceived helpfulness of answers given based on the implemented intent detection. Finally, we report on a user study investigating users’ evaluation of the two types of interactive explanations proposed (GUI and chatbot), and to test the effect of varying degrees of interactivity that result in greater or lesser access to explanatory information. By using Structural Equation Modeling, we reveal details on the relationships between the perceived quality of an explanation and the explanatory objectives of transparency, trust, and effectiveness. Our results show that providing interactive options for scrutinizing explanatory arguments has a significant positive influence on the evaluation by users (compared to low interactive alternatives). Results also suggest that user characteristics such as decision-making style may have a significant influence on the evaluation of different types of interactive explanation interfaces.

A Unified Approach to Nested and Non-Nested Slots for Spoken Language Understanding

As chatbots become more popular, multi-intent spoken language understanding (SLU) has received unprecedented attention. Multi-intent SLU, which primarily comprises the two subtasks of multiple intent detection (ID) and slot filling (SF), has the potential for widespread implementation. The two primary issues with the current approaches are as follows: (1) They cannot solve the problem of slot nesting; (2) The performance and inference rate of the model are not high enough. To address these issues, we suggest a multi-intent joint model based on global pointers to handle nested and non-nested slots. Firstly, we constructed a multi-dimensional type-slot label interaction network (MTLN) for subsequent intent decoding to enhance the implicit correlation between intents and slots, which allows for more adequate information about each other. Secondly, the global pointer network (GP) was introduced, which not only deals with nested and non-nested slots and slot incoherence but also has a faster inference rate and better performance than the baseline model. On two multi-intent datasets, the proposed model achieves state-of-the-art results on MixATIS with 1.6% improvement of intent Acc, 0.1% improvement of slot F1 values, 3.1% improvement of sentence Acc values, and 1.2%, 1.1% and 4.5% performance improvements on MixSNIPS, respectively. Meanwhile, the inference rate is also improved.

ReCoMIF: Reading comprehension based multi-source information fusion network for Chinese spoken language understanding

Spoken language understanding (SLU) plays a crucial role in the performance of dialogue systems. It usually includes slot filling and intent detection (SFID) tasks aiming at semantic parsing of utterances. At present, researchers focus mainly on English SLU tasks, while such investigations on Chinese utterances are not sufficient. In this paper, we first propose a reading comprehension based multi-source information fusion network, called ReCoMIF for Chinese SFID tasks by transforming the SLU task into a multi-turn question answering procedure comprising multiple choice for intent detection and span extraction for slot filling. Moreover, we present a TCM_CLS module with a concise architecture composed of TextCNN, MaxPooling, and feed forward network plus the [CLS] representation. Such three TCM_CLS modules are stacked in the proposed ReCoMIF network that can serve as sufficient integration of multi-source information originating from contexts, reading comprehension based queries, and hidden representations concerning intent and slot semantics. Finally, experimental results and ablation studies on three Chinese SLU datasets show that our proposed model can effectively fuse intent and slot information achieved by\\ state-of-the-art performance compared with other baseline methods.

Joint agricultural intent detection and slot filling based on enhanced heterogeneous attention mechanism

Agricultural diseases and pests are important factors restricting global food production. The knowledge-based question-answering (Q&A) system opens a new avenue for pest and disease control. As the key issue of Q&A, Natural language understanding (NLU) mainly includes two core subtasks, intent detection and slot filling (IDSF). However, the IDSF tasks in the agricultural field were still regarded as two completely independent subtasks, which may result in challenging problems such as error propagation and correlation neglection. To tackle the abovementioned consideration, address the gap in joint IDSF in the agricultural field, and finally improve the performance of agricultural question answering, a novel joint IDSF model based on enhanced heterogeneous attention mechanism for agriculture (AgHA-IDSF) was proposed. Its improvements mainly include three aspects. First, the heterogeneous attention mechanism was proposed to enable slot semantic representation to fully integrate intention information. Then, intent embedding matrix was introduced to provide rich intent information for guiding slot filling tasks. Finally, a weighting operation was designed to improve the guiding ability of related intents to slot filling task, while weakening the interference of irrelevant intents in a pre-trained paradigm. Furthermore, the first IDSF dataset for agriculture named AGIS was also collected and annotated, which contains 22 types of intents, 10 types of slots, and 11,976 samples. In addition, two publicly available datasets ATIS and SNIPS were also considered as benchmark datasets. The experimental results, including macro, micro, and ablation study et al., showed that the proposed model has achieved the optimal overall accuracy on three datasets, which were 88.41%, 87.57%, and 90.57%, respectively, even better than bidirectional association models, indicating the effectiveness of the proposed model in joint agricultural intent detection and slot filling.

Learning-Based Motion-Intention Prediction for End-Point Control of Upper-Limb-Assistive Robots

The lack of intuitive and active human-robot interaction makes it difficult to use upper-limb-assistive devices. In this paper, we propose a novel learning-based controller that intuitively uses onset motion to predict the desired end-point position for an assistive robot. A multi-modal sensing system comprising inertial measurement units (IMUs), electromyographic (EMG) sensors, and mechanomyography (MMG) sensors was implemented. This system was used to acquire kinematic and physiological signals during reaching and placing tasks performed by five healthy subjects. The onset motion data of each motion trial were extracted to input into traditional regression models and deep learning models for training and testing. The models can predict the position of the hand in planar space, which is the reference position for low-level position controllers. The results show that using IMU sensor with the proposed prediction model is sufficient for motion intention detection, which can provide almost the same prediction performance compared with adding EMG or MMG. Additionally, recurrent neural network (RNN)-based models can predict target positions over a short onset time window for reaching motions and are suitable for predicting targets over a longer horizon for placing tasks. This study's detailed analysis can improve the usability of the assistive/rehabilitation robots.

EEG Correlates of suicidal intentions in depressive patients who survived and have not been ill with COVID-19

IntroductionThe COVID-19 pandemic has caused an increase in suicidal intentions and attempts. One of the ways to prevent suicides is the timely detection of suicidal intentions. In this regard, it seems relevant to search for objective markers of suicidal intentions.ObjectivesThe aim of the study is to identify EEG correlates of suicidal intentions in depressive patients who survived and not have been ill with COVID-19.MethodsThe study included 30 female patients aged 16-25 years with depressive disorders (F31.3-4, F21.3-4 + F34.0, according to ICD-10), who had previously a mild or asymptomatic coronavirus infection (group “COVID”), and 40 depressive patients, matched by sex, age, syndrome and the pre-treatment severity of depression (according to the total score of the Hamilton HDRS-17 scale), but not sick with COVID-19 (“non-COVID” group, The severity of suicidal intentions was quantified by the number of points of item 3 of the HDRS-17 scale. All patients underwent pre-treatment registration of the background EEG in a state of quiet wakefulness with eyes closed in 16 monopolar leads: F7, F3, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1 and O2. The analysis of the absolute EEG spectral power (SP) was carried out in 8 narrow frequency sub-bands. Statistical processing of the data obtained was carried out using the methods of descriptive statistics, comparison of means and correlation analysis of the IBM SPSS Statistics, v.22 software package.ResultsIn the “COVID” group, the mean number of points of item 3 of the HDRS-17 scale was slightly higher than in the “non-COVID” group (2.1 ± 1.5 and 1.8 ± 1.6, respectively), but these differences did not reach the level of statistical significance (p> 0.05). However, the structure of correlations between the values of this indicator of the severity of suicidal intentions and EEG parameters in the two groups turned out to be different. In the “COVID” group, the largest number of significantly (p<0.05) positive correlation coefficients was noted between the number of points of item 3 of the HDRS-17 scale and the SP values of slow-wave EEG components, e.g. theta2 sub-band (6-8 Hz) in leads F7, F3, F4, F8, T4, C3, C4, P3 and P4, as well as the delta sub-band (2-4 Hz) in leads F7 and P4. In the “non-COVID” group, the number of points of item 3 of the HDRS-17 scale positively correlated with the SP values of the alpha2 sub-band (9-11 Hz) in leads F7, F8, F4, C3, C4, T4, P4 and O2, as well as with SP values of the delta sub-band (2-4 Hz) in leads F7, F3 and C3.ConclusionsIn depressive patients who underwent COVID-19, the severity of suicidal intentions is associated with EEG signs of a more reduced functional state of the cerebral cortex, including the anterior regions, than in patients who did not have coronavirus infection.The study supported by the RSF grant No. 21-18-00129.Disclosure of InterestNone Declared

Sensors and algorithms for locomotion intention detection of lower limb exoskeletons.

In recent years, lower limb exoskeletons (LLEs) have received much attention due to the potential to help people with paraplegia regain the ability of upright-legged locomotion. However, one major hindrance to converting prototypes into actual products is the lack of a balance recovery function. Locomotion intentions can be the first step for balance assistance. Therefore, its significance continues to grow. Many researchers focus on this topic, but there is a lack of a general discussion on the research phenomenon. Therefore, the purpose of this work is to systematize these data and benefit future research. This review is divided into two parts, the location of sensors/devices and the evaluation criteria of algorithms, which are the main components of locomotion intentions. We found that sensor/device placement is still concentrated in the lower limbs, but most researchers have found the importance of the chest. The peak power of the signal collected from the chest may be overestimated because it undergoes higher vertical velocity and acceleration during a rotation. However, despite the differences in peak power between the upper and lower back, high correlations were found for the tasks, especially from sitting to standing. Since peak power is based on vertical acceleration and velocity, it can be considered a metric that is more robust to changes in sensor location. Therefore, data acquisition from the chest is effective. In this paper, it is pointed out that sensors placed on the chest may have a tendency to change, as some researchers have realized in the field of locomotion intention recognition. In the evaluation criteria, we also found that deep learning algorithm (such as Back Propagation Artificial Neural Network (BPANN)) is outstanding, and Support Vector Machine (SVM) is the most cost-effective algorithm. In terms of accuracy, sensitivity, and specificity, BPANN achieved nearly 100%. SVM has different types; the best one achieves 98% accuracy, 100% sensitivity, and 100% specificity. But it also has 87.8% accuracy, which is not stable. Convolutional Neural Networks (CNN) can be used for image classification and have an accuracy of around 87%. Compared to the above two algorithms, CNN may have lower performance. Other algorithms also have higher accuracy, sensitivity, and specificity. These evaluation criteria, however, were not all ideal at the same time. Based on these results, we also point out the existing problems. In general, the application of these algorithms to LLE can contribute to its intention recognition, which can be helpful in balancing research. Finally, this can help make LLE more suitable for daily use.

Window length insensitive real-time EMG hand gesture classification using entropy calculated from globally parsed histograms

Electromyography (EMG) signal classification is vital to diagnose musculoskeletal abnormalities and control devices by motion intention detection. Machine learning assists both areas by classifying conditions or motion intentions. This paper proposes a novel window length insensitive EMG classification method utilizing the Entropy feature. The main goal of this study is to show that entropy can be used as the only feature for fast real-time classification of EMG signals of hand gestures. The main goal of this study is to show that entropy can be used as the only feature for fast real-time classification of EMG signals of hand gestures. Additionally, the entropy feature can classify feature vectors of different sliding window lengths without including them in the training data. Many kinds of entropy feature succeeded in electroencephalography (EEG) and electrocardiography (ECG) classification research. However, to the best of our knowledge, the Entropy Feature proposed by Shannon stays untested for EMG classification to this day. All the machine learning models are tested on datasets NinaPro DB5 and the newly collected SingleMyo. As an initial analysis to test the entropy feature, classic Machine Learning (ML) models are trained on the NinaPro DB5 dataset. This stage showed that except for the K Nearest Neighbor (kNN) with high inference time, Support Vector Machines (SVM) gave the best validation accuracy. Later, SVM models trained with feature vectors created by 1 s (200 samples) sliding windows are tested on feature vectors created by 250 ms (50 samples) to 1500 ms (300 samples) sliding windows. This experiment resulted in slight accuracy differences through changing window length, indicating that the Entropy feature is insensitive to this parameter. Lastly, Locally Parsed Histogram (LPH), typical in standard entropy functions, makes learning hard for ML methods. Globally Parsed Histogram (GPH) was proposed, and classification accuracy increased from 60.35% to 89.06% while window length insensitivity is preserved. This study shows that Shannon’s entropy is a compelling feature with low window length sensitivity for EMG hand gesture classification. The effect of the GPH approach against an easy-to-make mistake LPH is shown. A real-time classification algorithm for the entropy features is tested on the newly created SingleMyo dataset.

Joint modeling method of question intent detection and slot filling for domain-oriented question answering system

PurposeIntent detection and slot filling are two important tasks in question comprehension of a question answering system. This study aims to build a joint task model with some generalization ability and benchmark its performance over other neural network models mentioned in this paper.Design/methodology/approachThis study used a deep-learning-based approach for the joint modeling of question intent detection and slot filling. Meanwhile, the internal cell structure of the long short-term memory (LSTM) network was improved. Furthermore, the dataset Computer Science Literature Question (CSLQ) was constructed based on the Science and Technology Knowledge Graph. The datasets Airline Travel Information Systems, Snips (a natural language processing dataset of the consumer intent engine collected by Snips) and CSLQ were used for the empirical analysis. The accuracy of intent detection and F1 score of slot filling, as well as the semantic accuracy of sentences, were compared for several models.FindingsThe results showed that the proposed model outperformed all other benchmark methods, especially for the CSLQ dataset. This proves that the design of this study improved the comprehensive performance and generalization ability of the model to some extent.Originality/valueThis study contributes to the understanding of question sentences in a specific domain. LSTM was improved, and a computer literature domain dataset was constructed herein. This will lay the data and model foundation for the future construction of a computer literature question answering system.

Detection of Motor Cerebral Activity After Median Nerve Stimulation During General Anesthesia (STIM-MOTANA): Protocol for a Prospective Interventional Study.

Accidental awareness during general anesthesia (AAGA) is defined as an unexpected awareness of the patient during general anesthesia. This phenomenon occurs in 1%-2% of high-risk practice patients and can cause physical suffering and psychological after-effects, called posttraumatic stress disorder. In fact, no monitoring techniques are satisfactory enough to effectively prevent AAGA; therefore, new alternatives are needed. Because the first reflex for a patient during an AAGA is to move, but cannot do so because of the neuromuscular blockers, we believe that it is possible to design a brain-computer interface (BCI) based on the detection of movement intention to warn the anesthetist. To do this, we propose to describe and detect the changes in terms of motor cortex oscillations during general anesthesia with propofol, while a median nerve stimulation is performed. We believe that our results could enable the design of a BCI based on median nerve stimulation, which could prevent AAGA. To our knowledge, no published studies have investigated the detection of electroencephalographic (EEG) patterns in relation to peripheral nerve stimulation over the sensorimotor cortex during general anesthesia. The main objective of this study is to describe the changes in terms of event-related desynchronization and event-related synchronization modulations, in the EEG signal over the motor cortex during general anesthesia with propofol while a median nerve stimulation is performed. STIM-MOTANA is an interventional and prospective study conducted with patients scheduled for surgery under general anesthesia, involving EEG measurements and median nerve stimulation at two different times: (1) when the patient is awake before surgery (2) and under general anesthesia. A total of 30 patients will receive surgery under complete intravenous anesthesia with a target-controlled infusion pump of propofol. The changes in event-related desynchronization and event-related synchronization during median nerve stimulation according to the various propofol concentrations for 30 patients will be analyzed. In addition, we will apply 4 different offline machine learning algorithms to detect the median nerve stimulation at the cerebral level. Recruitment began in December 2022. Data collection is expected to conclude in June 2024. STIM-MOTANA will be the first protocol to investigate median nerve stimulation cerebral motor effect during general anesthesia for the detection of intraoperative awareness. Based on strong practical and theoretical scientific reasoning from our previous studies, our innovative median nerve stimulation-based BCI would provide a way to detect intraoperative awareness during general anesthesia. Clinicaltrials.gov NCT05272202; https://clinicaltrials.gov/ct2/show/NCT05272202. PRR1-10.2196/43870.