Imagery Tasks Research Articles

Mapping and decoding cortical engagement during motor imagery, mental arithmetic, and silent word generation using MEG

Accurate quantification of cortical engagement during mental imagery tasks remains a challenging brain-imaging problem with immediate relevance to developing brain-computer interfaces. We analyzed magnetoencephalography (MEG) data from 18 individuals completing cued motor imagery, mental arithmetic, and silent word generation tasks. Participants imagined movements of both hands (HANDS) and both feet (FEET), subtracted two numbers (SUB), and silently generated words (WORD). The task-related cortical engagement was inferred from beta band (17-25 Hz) power decrements estimated using a frequency-resolved beamforming method. In the hands and feet motor imagery tasks, beta power consistently decreased in premotor and motor areas. In the word and subtraction tasks, beta-power decrements showed engagements in language and arithmetic processing within the temporal, parietal, and inferior frontal regions. A support vector machine classification of beta power decrements yielded high accuracy rates of 74 and 68% for classifying motor-imagery (HANDS vs. FEET) and cognitive (WORD vs. SUB) tasks, respectively. From the motor-versus-nonmotor contrasts, excellent accuracy rates of 85 and 80% were observed for hands-versus-word and hands-versus-sub, respectively. A multivariate Gaussian-process classifier provided an accuracy rate of 60% for the four-way (HANDS-FEET-WORD-SUB) classification problem. Individual task performance was revealed by within-subject correlations of beta-decrements. Beta-power decrements are helpful metrics for mapping and decoding cortical engagement during mental processes in the absence of sensory stimuli or overt behavioral outputs. Markers derived based on beta decrements may be suitable for rehabilitation purposes, to characterize motor or cognitive impairments, or to treat patients recovering from a cerebral stroke.

A Hybrid GCN and Filter-Based Framework for Channel and Feature Selection: An fNIRS-BCI Study

In this study, a channel and feature selection methodology is devised for brain-computer interface (BCI) applications using functional near-infrared spectroscopy (fNIRS). A graph convolutional network (GCN) is employed to select the appropriate and correlated fNIRS channels. Furthermore, in the feature extraction phase, the performance of two filter-based feature selection algorithms, (i) the minimum redundancy maximum relevance (mRMR) and (ii) ReliefF, is investigated. The five most commonly used temporal statistical features (i.e., mean, slope, maximum, skewness, and kurtosis) are used, whereas the conventional support vector machine (SVM) is utilized as a classifier for training and testing. The proposed methodology is validated using an available online dataset of motor imagery (left- and right-hand), mental arithmetic, and baseline tasks. First, the efficacy of the proposed methodology is shown for two-class BCI applications (i.e., left- vs. right-hand motor imagery and mental arithmetic vs. baseline). Second, the proposed framework is applied to four-class BCI applications (i.e., left- vs. right-hand motor imagery vs. mental arithmetic vs. baseline). The results show that the number of appropriate channels and features was significantly reduced, resulting in a significant increase in classification accuracy for both two-class and four-class BCI applications, respectively. Furthermore, both mRMR (i.e., 87.8% for motor imagery, 87.1% for mental arithmetic, and 78.7% for four-class) and ReliefF (i.e., 90.7% for motor imagery, 93.7% for mental arithmetic, and 81.6% for four-class) yielded high average classification accuracy p < 0.05 . However, the results of the ReliefF algorithm are more stable and significant.

Performance Evaluation of State-of-the-Art Texture Feature Extraction Techniques on Medical Imagery Tasks

Interpreting medical images is certainly a complex task which requires extensive knowledge [1]. According to Computer Aided Diagnosis (CAD) serves as a second opinion that will help radiologists in diagnosis and on the other hand Content-based Image Retrieval uses visual content to help users browse, search and retrieve similar medical images from a database based on the user’s interest [2-4]. The competency of the CBMIR system depends on feature extraction methods [5]. The textural features are very important to determine the content of a medical image. Textural features provide scenic depth, the spatial distribution of tonal variation, and surface orientation [6]. Therefore, this study seeks to compare and evaluate some of the hand-crafted texture feature extraction techniques in CBMIR. This is to help those concerned in enhancing CBIR systems to make informed decisions concerning the selection of the best textural feature extraction techniques. Since there is no clear indication of which of the various texture feature extraction techniques is best suited for a given performance metric when considering which of the techniques to choose for a particular study in CBMIR systems. The objective of this work, therefore, is to comparatively evaluate the performance of the following texture feature extraction techniques; Local Binary Pattern (LBP), Gabor Filter, Gray-Level Co-occurrence Matrix (GLCM), Haralick Descriptor, Features from Accelerated Segment Test (FAST) and Features from Accelerated Segment Test and Binary Robust Independent Elementary Features (FAST &BRIEF) using the metrics; precision, recall, f1-score, mean squared error (MSE), accuracy and time. These techniques are coupled with specific similarity measure to obtain results. The results showed that LBP, Haralick Descriptor, FAST, and GLCM had the best results in terms of (Precision and Accuracy), Time, F1-Score, and Recall respectively. LBP had 82.05% and 88.23% scores for precision and accuracy respectively. The following scores represent the performance of the Haralick descriptor, FAST, and GLCM models respectively; 0.88s, 38.7%, and 44.82%. These test scores are obtained from datasets ranging from 1k-10.5k. Aside from LBP outperforming the other 5 models mentioned, it still outperformed the following proposed models ' [7]’, ‘Tamura texture feature and wavelet transform combined with Hausdorff distance - [8]’, ‘ [9]’ in terms of (precision, accuracy, and recall) and (precision and recall) respectively and probably f1-score (since f1-score is the weighted average of precision and recall). It is believed that an ensemble of LBP, Haralick descriptors, and Support Vector Machine (SVM) can represent a robust system for both medical image retrieval and classification.

Motor imagery EEG signal classification with a multivariate time series approach

BackgroundElectroencephalogram (EEG) signals record electrical activity on the scalp. Measured signals, especially EEG motor imagery signals, are often inconsistent or distorted, which compromises their classification accuracy. Achieving a reliable classification of motor imagery EEG signals opens the door to possibilities such as the assessment of consciousness, brain computer interfaces or diagnostic tools. We seek a method that works with a reduced number of variables, in order to avoid overfitting and to improve interpretability. This work aims to enhance EEG signal classification accuracy by using methods based on time series analysis. Previous work on this line, usually took a univariate approach, thus losing the possibility to take advantage of the correlation information existing within the time series provided by the different electrodes. To overcome this problem, we propose a multivariate approach that can fully capture the relationships among the different time series included in the EEG data. To perform the multivariate time series analysis, we use a multi-resolution analysis approach based on the discrete wavelet transform, together with a stepwise discriminant that selects the most discriminant variables provided by the discrete wavelet transform analysisResultsApplying this methodology to EEG data to differentiate between the motor imagery tasks of moving either hands or feet has yielded very good classification results, achieving in some cases up to 100% of accuracy for this 2-class pre-processed dataset. Besides, the fact that these results were achieved using a reduced number of variables (55 out of 22,176) can shed light on the relevance and impact of those variables.ConclusionsThis work has a potentially large impact, as it enables classification of EEG data based on multivariate time series analysis in an interpretable way with high accuracy. The method allows a model with a reduced number of features, facilitating its interpretability and improving overfitting. Future work will extend the application of this classification method to help in diagnosis procedures for detecting brain pathologies and for its use in brain computer interfaces. In addition, the results presented here suggest that this method could be applied to other fields for the successful analysis of multivariate temporal data.

Why the clock ticks differently in Parkinson's disease: Insights from motor imagery and resting-state functional magnetic resonance imaging

In Parkinson's disease (PD), an impaired perception of suprasecond time intervals has been reported. From a neurobiological perspective, dopamine is thought to be an important mediator of timing. Nevertheless, it is still unclear whether timing deficits in PD occur mainly in the motor context and are associated with corresponding striatocortical loops. This study attempted to fill this gap by investigating time reproduction in the context of a motor imagery task, and its neurobiological correlates in resting-state networks of basal ganglia substructures in PD.Nineteen PD patients and 10 healthy controls therefore underwent two time reproduction tasks. In a motor imagery task, subjects were asked to walk down a corridor for 10 s and reproduce the time spent walking during motor imagery afterwards. In an auditory task, the subjects had to reproduce an acoustically presented time interval of 10 s. Subsequently, resting-state functional magnetic resonance imaging was performed and voxel-wise regressions were conducted between striatal functional connectivity and performance in the individual task at group level and compared between groups.Patients significantly misjudged the time interval in the motor imagery task and an auditory task in comparison to controls. Seed-to-voxel functional connectivity analysis of basal ganglia substructures revealed a significant association between striatocortical connectivity and motor imagery performance. PD patients showed a different pattern of associated striatocortical connections as indicated by significantly different regression slopes for connections of the right putamen and left caudate nucleus.In accordance with previous findings, our data confirm an impaired time reproduction of suprasecond time intervals in PD patients. Our data imply that deficits in time reproduction tasks are not specific to motor context but reflect a general time reproduction deficit. According to our findings, impaired performance in context of motor imagery is accompanied by a different configuration of striatocortical resting-state networks responsible for timing.

KCS-FCnet: Kernel Cross-Spectral Functional Connectivity Network for EEG-Based Motor Imagery Classification

This paper uses EEG data to introduce an approach for classifying right and left-hand classes in Motor Imagery (MI) tasks. The Kernel Cross-Spectral Functional Connectivity Network (KCS-FCnet) method addresses these limitations by providing richer spatial-temporal-spectral feature maps, a simpler architecture, and a more interpretable approach for EEG-driven MI discrimination. In particular, KCS-FCnet uses a single 1D-convolutional-based neural network to extract temporal-frequency features from raw EEG data and a cross-spectral Gaussian kernel connectivity layer to model channel functional relationships. As a result, the functional connectivity feature map reduces the number of parameters, improving interpretability by extracting meaningful patterns related to MI tasks. These patterns can be adapted to the subject's unique characteristics. The validation results prove that introducing KCS-FCnet shallow architecture is a promising approach for EEG-based MI classification with the potential for real-world use in brain-computer interface systems.

Activation of the brain during motor imagination task with auditory stimulation.

Auditory stimulation is one of the most important influence factors in the cognitive process. It is an important guiding role in cognitive motor process. However, previous studies on auditory stimuli mainly focused on the cognitive effects of auditory stimuli on the cortex, while the role of auditory stimuli in motor imagery tasks is still unclear. In order to explore the role of auditory stimuli in motor imagery tasks, we studied the EEG power spectrum distribution characteristics, frontal parietal mismatch negative (MMN) wave characteristics, and the Inter trial phase locking consistency (ITPC) characteristics of the prefrontal cognitive cortex and parietal motor cortex. In this study, 18 subjects were hired to complete the motor imagery tasks, induced by auditory stimuli of task related verbs and task independent nouns. EEG power spectrum analysis showed that the activity of the contralateral motor cortex was significantly increased under the stimulation of verbs, and the amplitude of mismatch negative wave was also significantly increased. ITPC is mainly concentrated in μ, α, and γ bands in the process of motor imagery task guided by the auditory stimulus of verbs, while it is mainly concentrated in the β band under the nouns stimulation. This difference may be due to the impact of auditory cognitive process on motor imagery. We speculate that there may be a more complex mechanism for the effect of auditory stimulation on the inter test phase lock consistency. When the stimulus sound has the corresponding meaning to the motor action, the parietal motor cortex may be more affected by the cognitive prefrontal cortex, thus changing its normal response mode. This mode change is due to the joint action of motor imagination, cognitive and auditory stimuli. This study provides new insight into the neural mechanism of motor imagery task guided by auditory stimuli, and provides more information on the activity characteristics of the brain network in motor imagery task by cognitive auditory stimulation.

Transposed Convolution as Alternative Preprocessor for Brain-Computer Interface Using Electroencephalogram

The implementation of a brain–computer interface (BCI) using electroencephalography typically entails two phases: feature extraction and classification utilizing a classifier. Consequently, there are numerous disordered combinations of feature extraction and classification techniques that apply to each classification target and dataset. In this study, we employed a neural network as a classifier to address the versatility of the system in converting inputs of various forms into outputs of various forms. As a preprocessing step, we utilized a transposed convolution to augment the width of the convolution and the number of output features, which were then classified using a convolutional neural network (CNN). Our implementation of a simple CNN incorporating a transposed convolution in the initial layer allowed us to classify the BCI Competition IV Dataset 2a Motor Imagery Task data. Our findings indicate that our proposed method, which incorporates a two-dimensional CNN with a transposed convolution, outperforms the accuracy achieved without the transposed convolution. Additionally, the accuracy obtained was comparable to conventional optimal preprocessing methods, demonstrating the effectiveness of the transposed convolution as a potential alternative for BCI preprocessing.

Stuck in the past? Rumination-related memory integration

Memories connected to ruminative concerns repetitively capture attention, even in situations designed to alter them. However, recent research on memory updating suggests that memory for benign substitutes (e.g., reinterpretations) might be facilitated by integration with the ruminative memories. As a first approach, two experiments (Ns = 72) mimicked rumination-related memories with rumination-themed stimuli and an imagery task. College undergraduates screened for ruminative status first studied and imaged ruminative cue-target word pairs, and then in a second phase they studied the same cues re-paired with benign targets (along with new and repeated pairs). On the test of cued recall of benign targets, they judged whether each recalled word had been repeated or changed across the two phases (or was new in the second phase). When target changes were not remembered, recall of benign targets revealed proactive interference that was insensitive to ruminative status. However, when participants remembered change and the ruminative targets, their recall of benign targets was facilitated, particularly if they identified as ruminators (Experiment 1). When the test simply asked for recall of either or both targets (Experiment 2), ruminators recalled both targets more frequently than did others. These outcomes suggest that ruminative memories might provide bridges to remembering associated benign memories, such as reinterpretations, under conditions consistent with everyday ruminative retrieval.

Low-level language processing in brain-injured patients.

Assessing cognitive function-especially language processing-in severely brain-injured patients is critical for prognostication, care, and development of communication devices (e.g. brain-computer interfaces). In patients with diminished motor function, language processing has been probed using EEG measures of command-following in motor imagery tasks. While such tests eliminate the need for motor response, they require sustained attention. However, passive listening tasks, with an EEG response measure can reduce both motor and attentional demands. These considerations motivated the development of two assays of low-level language processing-identification of differential phoneme-class responses and tracking of the natural speech envelope. This cross-sectional study looks at a cohort of 26 severely brain-injured patient subjects and 10 healthy controls. Patients' level of function was assessed via the coma recovery scale-revised at the bedside. Patients were also tested for command-following via EEG and/or MRI assays of motor imagery. For the present investigation, EEG was recorded while presenting a 148 s audio clip of Alice in Wonderland. Time-locked EEG responses to phoneme classes were extracted and compared to determine a differential phoneme-class response. Tracking of the natural speech envelope was assessed from the same recordings by cross-correlating the EEG response with the speech envelope. In healthy controls, the dynamics of the two measures were temporally similar but spatially different: a central parieto-occipital component of differential phoneme-class response was absent in the natural speech envelope response. The differential phoneme-class response was present in all patient subjects, including the six classified as vegetative state/unresponsive wakefulness syndrome by behavioural assessment. However, patient subjects with evidence of language processing either by behavioural assessment or motor imagery tests had an early bilateral response in the first 50 ms that was lacking in patient subjects without any evidence of language processing. The natural speech envelope tracking response was also present in all patient subjects and responses in the first 100 ms distinguished patient subjects with evidence of language processing. Specifically, patient subjects with evidence of language processing had a more global response in the first 100 ms whereas those without evidence of language processing had a frontopolar response in that period. In summary, we developed two passive EEG-based methods to probe low-level language processing in severely brain-injured patients. In our cohort, both assays showed a difference between patient subjects with evidence of command-following and those with no evidence of command-following: a more prominent early bilateral response component.

Classification of Right/Left Hand Motor Imagery by Effective Connectivity Based on Transfer Entropy in Electroencephalogram Signal.

The right and left-hand motor imagery (MI) analysis based on the electroencephalogram (EEG) signal can directly link the central nervous system to a computer or a device. This study aims to identify a set of robust and nonlinear effective brain connectivity features quantified by transfer entropy (TE) to characterize the relationship between brain regions from EEG signals and create a hierarchical feature selection and classification for discrimination of right and lefthand MI tasks. TE is calculated among EEG channels as the distinctive, effective connectivity features. TE is a model-free method that can measure nonlinear effective connectivity and analyze multivariate dependent directed information flow among neural EEG channels. Then four feature subset selection methods namely relief-F, Fisher, Laplacian, and local learningbased clustering (LLCFS) algorithms are used to choose the most significant effective connectivity features and reduce redundant information. Finally, support vector machine (SVM) and linear discriminant analysis (LDA) methods are used for classification. Results show that the best performance in 29 healthy subjects and 60 trials is achieved using the TE method via the Relief-F algorithm as feature selection and support vector machine (SVM) classification with 91.02% accuracy. The TE index and a hierarchical feature selection and classification can be useful for the discrimination of right- and left-hand MI tasks from multichannel EEG signals. Effective connectivity features were extracted from electroencephalogram (EEG) to analyze relationships between regions.Four feature selection methods used to select most significant effective features.Support vector machine (SVM) used for discrimination of right and left hand motor imagery (MI) task. In this study, we investigated brain activity using effective connectivity during MI task based on EEG signals. The motor imagery task can accomplish the same goal as motor execution, since they are both activated by the same brain area. Transfer entropy, coherence, and Granger casualty were employed to extract the features. Differential patterns of activity between the left vs. right MI task showed activity around the motor area rather than other areas. In order to reduce redundant information and select the most significant effective connectivity features, four feature subset selection algorithms are used: Relief-F, Fisher, Laplacian, and learning-based clustering feature selection (LLCFS). Then, support vector machine (SVM) and linear discriminant analysis (LDA) are used to classify left and right hand MI task. Comparison of three different connectivity methods showed that TE index had the highest classification accuracy, and could be useful for the discrimination of right and left hand MI tasks from multichannel EEG signals.

Neuronal representation of bimanual arm motor imagery in the motor cortex of a tetraplegia human, a pilot study.

How the human brain coordinates bimanual movements is not well-established. Here, we recorded neural signals from a paralyzed individual's left motor cortex during both unimanual and bimanual motor imagery tasks and quantified the representational interaction between arms by analyzing the tuning parameters of each neuron. We found a similar proportion of neurons preferring each arm during unimanual movements, however, when switching to bimanual movements, the proportion of contralateral preference increased to 71.8%, indicating contralateral lateralization. We also observed a decorrelation process for each arm's representation across the unimanual and bimanual tasks. We further confined that these changes in bilateral relationships are mainly caused by the alteration of tuning parameters, such as the increased bilateral preferred direction (PD) shifts and the significant suppression in bilateral modulation depths (MDs), especially the ipsilateral side. These results contribute to the knowledge of bimanual coordination and thus the design of cutting-edge bimanual brain-computer interfaces.

E‐CNNet: Time‐reassigned Multisynchrosqueezing transform‐based deep learning framework for MI‐BCI task classification

AbstractThe classification of electroencephalograms‐based motor imagery signals poses a significant issue in the design and development of brain‐computer interfaces. Neural Networks are observed to be successful in classification of motor imagery brain signals. However, the existing motor imagery data sets are of limited size and suffer from low signal to noise ratio. Hence, to achieve high performance with small datasets, this paper proposes a novel combination of time frequency analysis along with deep learning network to perform the brain signal classification task. The proposed framework consists of two parts: (1) Time‐reassigned Multisynchrosqueezing Transform to efficiently capture the dynamic properties of non‐stationary EEG; and (2) A new hybrid model E‐CNNet is proposed for feature extraction and classification of brain signals. A robust classification pipeline is constructed to classify the extracted features into respective motor imagery tasks. Ensemble of boosting classifiers is used to generate the final predictions, thus, reducing the variance and bias of individual classifiers. The performance of the proposed methodology is evaluated using two publicly available datasets: BCI competition III, dataset IIIa and BCI competition IV, dataset IIa. We have obtained the average classification accuracy of 94.44% on BCI competition III, dataset IIIa and 89.25% on BCI competition IV, dataset IIa.

Recognition of single upper limb motor imagery tasks from EEG using multi-branch fusion convolutional neural network.

Motor imagery-based brain-computer interfaces (MI-BCI) have important application values in the field of neurorehabilitation and robot control. At present, MI-BCI mostly use bilateral upper limb motor tasks, but there are relatively few studies on single upper limb MI tasks. In this work, we conducted studies on the recognition of motor imagery EEG signals of the right upper limb and proposed a multi-branch fusion convolutional neural network (MF-CNN) for learning the features of the raw EEG signals as well as the two-dimensional time-frequency maps at the same time. The dataset used in this study contained three types of motor imagery tasks: extending the arm, rotating the wrist, and grasping the object, 25 subjects were included. In the binary classification experiment between the grasping object and the arm-extending tasks, MF-CNN achieved an average classification accuracy of 78.52% and kappa value of 0.57. When all three tasks were used for classification, the accuracy and kappa value were 57.06% and 0.36, respectively. The comparison results showed that the classification performance of MF-CNN is higher than that of single CNN branch algorithms in both binary-class and three-class classification. In conclusion, MF-CNN makes full use of the time-domain and frequency-domain features of EEG, can improve the decoding accuracy of single limb motor imagery tasks, and it contributes to the application of MI-BCI in motor function rehabilitation training after stroke.

Multi-classification for EEG motor imagery signals using data evaluation-based auto-selected regularized FBCSP and convolutional neural network

In recent years, there has been a renewal of interest in brain–computer interface (BCI). One of the BCI tasks is to classify the EEG motor imagery (MI). A great deal of effort has been made on MI classification. What seems to be lacking, however, is multiple MI classification. This paper develops a single-channel-based convolutional neural network to tackle multi-classification motor imagery tasks. For multi-classification, a single-channel learning strategy can extract effective information from each independent channel, making the information between adjacent channels not affect each other. A data evaluation method and a mutual information-based regularization parameters auto-selection algorithm are also proposed to generate effective spatial filters. The proposed method can be used to tackle the problem of an inaccurate mixed covariance matrix caused by fixed regularization parameters and invalid training data. To illustrate the merits of the proposed methods, we used the tenfold cross-validation accuracy and kappa as the evaluation measures to test two data sets. BCI4-2a and BCI3a data sets have four mental classes. For the BCI4-2a data set, the average accuracy is 79.01%, and the kappa is 0.7202 using data evaluation-based auto-selected filter bank regularized common spatial pattern voting (D-ACSP-V) and single-channel series convolutional neural network (SCS-CNN). Compared to traditional FBRCSP, the proposed method improved accuracy by 7.14% for the BCI4-2a data set. By using the BCI3a data set, the proposed method improved accuracy by 9.54% compared with traditional FBRCSP, the average accuracy of the proposed method is 83.70%, and the kappa is 0.7827.

Neurofeedback-dependent influence of the ventral striatum using a working memory paradigm targeting the dorsolateral prefrontal cortex.

Executive functions and motivation have been established as key aspects for neurofeedback success. However, task-specific influence of cognitive strategies is scarcely explored. In this study, we test the ability to modulate the dorsolateral prefrontal cortex, a strong candidate for clinical application of neurofeedback in several disorders with dysexecutive syndrome, and investigate how feedback contributes to better performance in a single session. Participants of both neurofeedback (n = 17) and sham-control (n = 10) groups were able to modulate DLPFC in most runs (with or without feedback) while performing a working memory imagery task. However, activity in the target area was higher and more sustained in the active group when receiving feedback. Furthermore, we found increased activity in the nucleus accumbens in the active group, compared with a predominantly negative response along the block in participants receiving sham feedback. Moreover, they acknowledged the non-contingency between imagery and feedback, reflecting the impact on motivation. This study reinforces DLPFC as a robust target for neurofeedback clinical implementations and enhances the critical influence of the ventral striatum, both poised to achieve success in the self-regulation of brain activity.

Is Future Mental Imagery Associated with Reduced Impact of the COVID-19 Pandemic on Negative Affect and Anhedonic Symptoms in Young People?

BackgroundDifficulties with prospective mental images are associated with adolescent depression. Current treatments mainly focus on verbal techniques to reduce negative affect (e.g. low mood) rather than enhancing positive affect, despite anhedonia being present in adolescents. We investigated the concurrent relationships between the vividness of negative and positive prospective mental imagery and negative affect and positive affect; and examined whether negative and positive prospective mental imagery moderated the impact of recent stress (COVID-19-linked stress) on negative and positive affect.Methods2602 young people (12–25 years) completed the Prospective Imagery Task and self-reported on symptoms of negative affect, anhedonia and COVID-19 linked stress.ResultsElevated vividness of negative future mental imagery and reduced vividness of positive future mental imagery were associated with increased negative affect, whereas only reduced vividness of positive future imagery was associated with increased symptoms of anhedonia. Elevated vividness of negative future images amplified the association between COVID-19 linked stress and negative affect, while elevated vividness of positive future images attenuated the association between COVID-19 linked stress and anhedonia.ConclusionsFuture mental imagery may be differentially associated with negative and positive affect, but this needs to be replicated in clinical populations to support novel adolescent psychological treatments.

Impacts of simplifying articulation movements imagery to speech imagery BCI performance

Objective. Speech imagery (SI) can be used as a reliable, natural, and user-friendly activation task for the development of brain-computer interface (BCI), which empowers individuals with severe disabilities to interact with their environment. The functional near-infrared spectroscopy (fNIRS) is advanced as one of the most suitable brain imaging methods for developing BCI systems owing to its advantages of being non-invasive, portable, insensitive to motion artifacts, and having relatively high spatial resolution. Approach. To improve the classification performance of SI BCI based on fNIRS, a novel paradigm was developed in this work by simplifying the articulation movements in SI to make the articulation movement differences clearer between different words imagery tasks. A SI BCI was proposed to directly answer questions by covertly rehearsing the word ‘是’ or ‘否’ (‘yes’ or ‘no’ in English), and an unconstrained rest task also was contained in this BCI. The articulation movements of SI were simplified by retaining only the movements of the jaw and lips of vowels in Chinese Pinyin for words ‘是’ and ‘否’. Main results. Compared with conventional speech imagery, simplifying the articulation movements in SI could generate more different brain activities among different tasks, which led to more differentiable temporal features and significantly higher classification performance. The average 3-class classification accuracies of the proposed paradigm across all 20 participants reached 69.6% and 60.2% which were about 10.8% and 5.6% significantly higher than those of the conventional SI paradigm operated in the 0–10 s and 0–2.5 s time windows, respectively. Significance. These results suggested that simplifying the articulation movements in SI is promising for improving the classification performance of intuitive BCIs based on speech imagery.

Repetition effects in memory and truth assessments through the lens of transfer-appropriate processing

ABSTRACT The Transfer-appropriate Processing (TAP) framework has demonstrated enhanced recognition memory when processing operations engaged at encoding and at test match. Our research applied TAP to study the illusory truth effect (ITE). We investigated whether the match/mismatch of evaluative goals at encoding and at test affects the ITE. At encoding, participants saw target words (Experiments 1–3; or full trivia claims Experiments 4–5) and completed an evaluative goal: imagery task or vowel-counting. At test, participants saw target words embedded in trivia claims that were old or new and completed the same (matching) or different (mismatching) evaluative goal that they completed at encoding, before making truth or memory ratings. We found a typical TAP effect for memory judgements when people saw words at encoding, but no TAP effect when people saw claims at encoding. We also found an ITE when people saw claims at encoding, but no ITE when people saw words at encoding (no evidence of TAP moderating truth judgments). Together these results extend both the TAP and ITE literatures, suggesting boundary conditions for TAP and the conditions under which the ITE emerges.

An overview of methods of left and right foot motor imagery based on Tikhonov regularisation common spatial pattern.

The motor imagery brain-computer interface (MI-BCI) provides an interactive control channel for spinal cord injury patients. However, the limitations of feature extraction algorithms may lead to low accuracy and instability in decoding electroencephalogram (EEG) signals. In this study, we examined the classification performance of an MI-BCI system by focusing on the distinction of the left and right foot kinaesthetic motor imagery tasks in five subjects. Feature extraction was performed using the common space pattern (CSP) and the Tikhonov regularisation CSP (TRCSP) spatial filters. TRCSP overcomes the CSP problems of noise sensitivity and overfitting. Moreover, support vector machine (SVM) and linear discriminant analysis (LDA) were used for classification and recognition. We constructed four combined classification methods (TRCSP-SVM, TRCSP-LDA, CSP-SVM, and CSP-LDA) and evaluated them by comparing their accuracies, kappa coefficients, and receiver operating characteristic (ROC) curves. The results showed that the TRCSP-SVM method performed significantly better than others (average accuracy 97%, average kappa coefficient 0.91, and average area under ROC curve (AUC) 0.98). Using TRCSP instead of standard CSP improved accuracy by up to 10%. This study provides insights into the classification of EEG signals. The results of this study can aid lower limb MI-BCI systems in rehabilitation training.