No clear evidence of a difference between individuals who self-report an absence of auditory imagery and typical imagers on auditory imagery tasks.

Aphantasia is characterised by the inability to create mental images in one's mind. Studies investigating impairments in imagery typically focus on the visual domain. However, it is possible to generate many different forms of imagery including imagined auditory, kinesthetic, tactile, motor, taste and other experiences. Recent studies show that individuals with aphantasia report a lack of imagery in modalities, other than vision, including audition. However, to date, no research has examined whether these reductions in self-reported auditory imagery are associated with decrements in tasks that require auditory imagery. Understanding the extent to which visual and auditory imagery deficits co-occur can help to better characterise the core deficits of aphantasia and provide an alternative perspective on theoretical debates on the extent to which imagery draws on modality-specific or modality-general processes. In the current study, individuals that self-identified as being aphantasic and matched control participants with typical imagery performed two tasks: a musical pitch-based imagery and voice-based categorisation task. The majority of participants with aphantasia self-reported significant deficits in both auditory and visual imagery. However, we did not find a concomitant decrease in performance on tasks which require auditory imagery, either in the full sample or only when considering those participants that reported significant deficits in both domains. These findings are discussed in relation to the mechanisms that might obscure observation of imagery deficits in auditory imagery tasks in people that report reduced auditory imagery.

Aphantasia and involuntary imagery.

Aphantasia is a condition that is often characterized as the impaired ability to create voluntary mental images. Aphantasia is assumed to selectively affect voluntary imagery mainly because even though aphantasics report being unable to visualize something at will, many report having visual dreams. We argue that this common characterization of aphantasia is incorrect. Studies on aphantasia are often not clear about whether they are assessing voluntary or involuntary imagery, but some studies show that several forms of involuntary imagery are also affected in aphantasia (including imagery in dreams). We also raise problems for two attempts to show that involuntary images are preserved in aphantasia. In addition, we report the results of a study about afterimages in aphantasia, which suggest that these tend to be less intense in aphantasics than in controls. Involuntary imagery is often treated as a unitary kind that is either present or absent in aphantasia. We suggest that this approach is mistaken and that we should look at different types of involuntary imagery case by case. Doing so reveals no evidence of preserved involuntary imagery in aphantasia. We suggest that a broader characterization of aphantasia, as a deficit in forming mental imagery, whether voluntary or not, is more appropriate. Characterizing aphantasia as a volitional deficit is likely to lead researchers to give incorrect explanations for aphantasia, and to look for the wrong mechanisms underlying it.

Self-supervised contrastive learning for EEG-based cross-subject motor imagery recognition.

Objective. The extensive application of electroencephalography (EEG) in brain-computer interfaces (BCIs) can be attributed to its non-invasive nature and capability to offer high-resolution data. The acquisition of EEG signals is a straightforward process, but the datasets associated with these signals frequently exhibit data scarcity and require substantial resources for proper labeling. Furthermore, there is a significant limitation in the generalization performance of EEG models due to the substantial inter-individual variability observed in EEG signals.Approach. To address these issues, we propose a novel self-supervised contrastive learning framework for decoding motor imagery (MI) signals in cross-subject scenarios. Specifically, we design an encoder combining convolutional neural network and attention mechanism. In the contrastive learning training stage, the network undergoes training with the pretext task of data augmentation to minimize the distance between pairs of homologous transformations while simultaneously maximizing the distance between pairs of heterologous transformations. It enhances the amount of data utilized for training and improves the network's ability to extract deep features from original signals without relying on the true labels of the data.Main results. To evaluate our framework's efficacy, we conduct extensive experiments on three public MI datasets: BCI IV IIa, BCI IV IIb, and HGD datasets. The proposed method achieves cross-subject classification accuracies of 67.32%, 82.34%, and 81.13%on the three datasets, demonstrating superior performance compared to existing methods.Significance. Therefore, this method has great promise for improving the performance of cross-subject transfer learning in MI-based BCI systems.

Land-use classification based on high-resolution remote sensing imagery and deep learning models.

High-resolution imagery and deep learning models have gained increasing importance in land-use mapping. In recent years, several new deep learning network modeling methods have surfaced. However, there has been a lack of a clear understanding of the performance of these models. In this study, we applied four well-established and robust deep learning models (FCN-8s, SegNet, U-Net, and Swin-UNet) to an open benchmark high-resolution remote sensing dataset to compare their performance in land-use mapping. The results indicate that FCN-8s, SegNet, U-Net, and Swin-UNet achieved overall accuracies of 80.73%, 89.86%, 91.90%, and 96.01%, respectively, on the test set. Furthermore, we assessed the generalization ability of these models using two measures: intersection of union and F1 score, which highlight Swin-UNet's superior robustness compared to the other three models. In summary, our study provides a systematic analysis of the classification differences among these four deep learning models through experiments. It serves as a valuable reference for selecting models in future research, particularly in scenarios such as land-use mapping, urban functional area recognition, and natural resource management.

Working mechanisms of imagery rescripting (ImRs) in adult patients with childhood-related PTSD: a pilot study.

Background: Imagery rescripting (ImRs) has shown to be an effective treatment for posttraumatic stress disorders (PTSD) resulting from childhood-related trauma. The current theory is that the change of meaning of the trauma memory is central to the treatment. Several authors have suggested that the expression of needs, feelings and actions may act as potential healing factors, but little specific research aimed at (in)validating this hypothesis has been done so far.Objective: In this study we investigated to what extent the expression of inhibited action tendencies and the fulfilling of needs lead to the reduction of PTSD symptoms in clients with early childhood trauma.Method: Recordings of 249 therapy sessions of 24 ImRs treatments were rated with an observation instrument developed for this purpose, after which the scores were related to pre and posttreatment symptoms, assessed with the Impact of Events Scale-Revised (IES-R).Results: Scores on the IES-R decreased from pretreatment to posttreatment. The two subscales of the NATS (At-scale and N-scale)significantly predicted the posttreatment scores on the IES-R after controlling for the influence of pretreatment IES-R scores: the better the expression of inhibited action tendencies and the better the fulfilling of needs, the lower the symptoms after treatment.Conclusions: This pilot study on the underlying mechanisms of ImRs in PTSD treatment has shown that the expression of action tendencies and fulfilling basic needs during ImRs are associated with a decrease in PTSD symptoms after treatment, and that actions and basic needs cannot be viewed separately. Follow-up research could focus on which of the six domains of the Needs and Action tendencies Scale (NATS) has the greatest effect on the reduction of PTSD symptoms. With this information we can further improve the ImRs protocol.

The pilot study of working mechanisms of imaginary rescripting shows that the NATS is a reliable research tool for observing expressed action tendencies and fulfilled needs.The better the action tendencies are expressed during treatment and the better the needs are fulfilled, the lower posttreatment symptoms.It seems useful if practitioners are specifically trained during the ImRs training in performing actions that lead to the fulfilment of basic needs.

Effect of PTSD treatment on cardiovascular reactivity during trauma memory recall and correspondence with symptom improvement.

ABSTRACTBackground: Prior research has shown PTSD treatment leads to reductions in cardiovascular reactivity during trauma recall, but the extent to which such reductions are associated with changes in PTSD symptoms is less clear. Moreover, such relationships have not been investigated in a cognitively focused PTSD treatment.Objective: To examine changes in cardiovascular reactivity to the trauma memory in patients receiving cognitive processing therapy (CPT), CPT with a written trauma account, and a written account only condition. We also examined the association of such changes with symptom improvement.Method: 118 women with PTSD secondary to interpersonal violence completed pre- and post-treatment assessments of PTSD symptoms and cardiovascular reactivity during a script-driven imagery task.Results: Results indicated a significant but modest reduction in cardiovascular reactivity in CPT conditions. Changes in cardiovascular reactivity and reexperiencing symptoms were significantly associated among the whole sample. Among individuals with the greatest reactivity to the trauma memory at pretreatment, associations were also seen with changes in total PTSD, numbing, and trauma-related guilt.Conclusions: Results indicate that previous findings on the effect of PTSD treatment on cardiovascular reactivity during trauma recall extend to cognitively oriented treatment. Baseline cardiovascular reactivity may influence the extent to which reductions in PTSD symptoms and reactivity during trauma recall are related.

Cognitive Processing Therapy leads to reduced heart rate reactivity when recalling a trauma memory.Decreases in heart rate reactivity are associated with reduced reexperiencing symptoms.Changes in heart rate reactivity and PTSD symptoms are more closely related among patients with greater pretreatment reactivity.

Upper Limb Electromyographic Responses to Motor Imagery and Action Observation in Acquired Brain Injury.

Acquired Brain Injuries are one of the leading causes of mortality and disability worldwide. One of the most frequent sequelae is motor impairment of the upper limbs, which affects people's functionality and quality of life. Following the discovery of mirror neurons, new techniques were developed based on the mechanisms of neuronal plasticity, such as motor imagery (MI) and action observation (AO). We propose a protocol using electromyographic recordings of forearm muscles in people who have suffered a stroke during an MI task and an AO task. Three different experimental conditions will be studied during the electromyographic recordings: control recording, recording during MI, and recording during AO. Understanding the muscle activation in each technique will allow us to develop future protocols and intervention plans, improving the quality of care for people who have suffered a stroke.

Classification of Motor Imagery EEG signals using high resolution time-frequency representations and convolutional neural network.

A Motor Imagery (MI) based Brain Computer Interface (BCI) system aims to provide neuro-rehabilitation for the motor disabled people and patients with brain injuries (e.g., stroke patients) etc. The aim of this work is to classify the left and right hand MI tasks by utilizing the occurrence of event related desynchronization and synchronization (ERD\ERS) in the Electroencephalogram (EEG) during these tasks. This study proposes to use a set of Complex Morlet Wavelets (CMW) having frequency dependent widths to generate high-resolution time-frequency representations (TFR) of the MI EEG signals present in the channels C3 and C4. A novel method for the selection of the value of number of cycles relative to the center frequency of the CMW is studied here for extracting the MI task features. The generated TFRs are given as input to a Convolutional neural network (CNN) for classifying them into left or right hand MI tasks. The proposed framework attains a classification accuracy of 82.2% on the BCI Competition IV dataset 2a, showing that the TFRs generated in this work give a higher classification accuracy than the baseline methods and other existing algorithms.

Stimulus-elicited involuntary autobiographical memories.

The reflexive imagery task (RIT) has been used to investigate stimulus-elicited involuntary mental processes. The task has been successful in eliciting involuntary perceptual experiences, urges, and even higher-order cognitions, but it has never been used to elicit autobiographical memories, even though in everyday life these memories are often activated involuntarily by external stimuli. These memories are different in interesting ways from the kinds of mental representations that have been activated involuntarily in the RIT. The memories have properties which might make them insusceptible to such a form of external influence. Perhaps substantive effects will not arise because the mental representations associated with autobiographical memories are complex, poly-sensory, and rich in terms of content. To investigate this matter, we developed a variant of the RIT in which participants were presented with external stimuli (line drawings of everyday objects) and instructed not to recall any autobiographical memories. We investigated whether the nature of the involuntary memories was influenced by the nature of the stimulus. In two experiments, the involuntary memories were associated to the stimulus on a majority of the trials (∼80%). We discuss theoretical implications of this finding and of identifying the conditions in which such involuntary effects will not arise. The boundary conditions of the RIT effect illuminate the limits of unconscious processing and also the role of conscious processing in nervous function.

A matter of precision? Scene imagery in individuals with high-functioning autism spectrum disorder.

The ability to create mental representations of scenes is essential for remembering, predicting, and imagining. In individuals with autism spectrum disorders (ASD) this ability may be impaired. Considering that autistic characteristics such as weak central coherence or reduced communication abilities may disadvantage autistic participants in traditional imagery tasks, this study attempted to use a novel task design to measure the ability of scene imagery. Thirty high-functioning adults with ASD and 27 non-autistic matched control adults were asked to describe imagined fictitious scenes using two types of scene imagery tasks. In a free imagery task, participants were asked to imagine a scene based on a given keyword. In a guided imagery task, participants had to imagine a scene based on a detailed description of the scene. Additionally, narrative abilities were assessed using the Narrative Scoring Scheme. Statistical analyses revealed no group effects in the free and guided imagery of fictional scenes. Participants with ASD performed worse than control participants in the narrative task. Narrative abilities correlated positively with performance in both imagery tasks in the ASD group only. Hence, individuals with ASD seem to show as good imagery abilities as non-autistic individuals. The results are discussed in the light of the differences between imagery and imagination and possible gender differences.

Individual-finger motor imagery classification: a data-driven approach with Shapley-informed augmentation.

Objective. Classifying motor imagery (MI) tasks that involve fine motor control of the individual five fingers presents unique challenges when utilizing electroencephalography (EEG) data. In this paper, we systematically assess the classification of MI functions for the individual five fingers using single-trial time-domain EEG signals. This assessment encompasses both within-subject and cross-subject scenarios, supported by data-driven analysis that provides statistical validation of the neural correlate that could potentially discriminate between the five fingers.Approach. We present Shapley-informed augmentation, an informed approach to enhance within-subject classification accuracy. This method is rooted in insights gained from our data-driven analysis, which revealed inconsistent temporal features encoding the five fingers MI across sessions of the same subject. To evaluate its impact, we compare within-subject classification performance both before and after implementing this augmentation technique.Main results. Both the data-driven approach and the model explainability analysis revealed that the parietal cortex contains neural information that helps discriminate the individual five fingers' MI apart. Shapley-informed augmentation successfully improved classification accuracy in sessions severely affected by inconsistent temporal features. The accuracy for sessions impacted by inconsistency in their temporal features increased by an average of26.3%±6.70, thereby enabling a broader range of subjects to benefit from brain-computer interaction (BCI) applications involving five-fingers MI classification. Conversely, non-impacted sessions experienced only a negligible average accuracy decrease of2.01±5.44%. The average classification accuracy achieved is around 60.0% (within-session), 50.0% (within-subject) and 40.0% (leave-one-subject-out).Significance. This research offers data-driven evidence of neural correlates that could discriminate between the individual five fingers MI and introduces a novel Shapley-informed augmentation method to address temporal variability of features, ultimately contributing to the development of personalized systems.

Psychophysiological strategies for enhancing performance through imagery-skin conductance level analysis in guided vs. self-produced imagery.

Athletes need to achieve their optimal level of arousal for peak performance. Visualization or mental rehearsal (i.e., Imagery) often helps to obtain an appropriate level of activation, which can be detected by monitoring Skin Conductance Level (SCL). However, different types of imagery could elicit different amount of physiological arousal. Therefore, this study aims: (1) to investigate differences in SCL associated with two instructional modalities of imagery (guided vs. self-produced) and six different scripts; (2) to check if SCL could differentiate respondents according to their sport expertise. Thirty participants, aged between 14 and 42 years (M = 22.93; SD = 5.24), with different sport levels took part in the study. Participants listened to each previously recorded script and then were asked to imagine the scene for a minute. During the task, SCL was monitored. We analysed the mean value, variance, slope and number of fluctuations per minute of the electrodermal signal. Unsupervised machine learning models were used for measuring the resemblance of the signal. The Wilcoxon signed-rank test was used for distinguishing guided and self-produced imagery, and The Mann-Whitney U test was used for distinguishing results of different level athletes. We discovered that among others, self-produced imagery generates lower SCL, higher variance, and a higher number of fluctuations compared to guided imagery. Moreover, we found similarities of the SCL signal among the groups of athletes (i.e. expertise level). From a practical point of view, our findings suggest that different imagery instructional modalities can be implemented for specific purposes of mental preparation.

Adaptive Time-Frequency Segment Optimization for Motor Imagery Classification.

Motor imagery (MI)-based brain-computer interface (BCI) has emerged as a crucial method for rehabilitating stroke patients. However, the variability in the time-frequency distribution of MI-electroencephalography (EEG) among individuals limits the generalizability of algorithms that rely on non-customized time-frequency segments. In this study, we propose a novel method for optimizing time-frequency segments of MI-EEG using the sparrow search algorithm (SSA). Additionally, we apply a correlation-based channel selection (CCS) method that considers the correlation coefficient of features between each pair of EEG channels. Subsequently, we utilize a regularized common spatial pattern method to extract effective features. Finally, a support vector machine is employed for signal classification. The results on three BCI datasets confirmed that our algorithm achieved better accuracy (99.11% vs. 94.00% for BCI Competition III Dataset IIIa, 87.70% vs. 81.10% for Chinese Academy of Medical Sciences dataset, and 87.94% vs. 81.97% for BCI Competition IV Dataset 1) compared to algorithms with non-customized time-frequency segments. Our proposed algorithm enables adaptive optimization of EEG time-frequency segments, which is crucial for the development of clinically effective motor rehabilitation.

Revisiting the blind mind: Still no evidence for sensory visual imagery in individuals with aphantasia.

The inability to visualise was given the name aphantasia in 2015 by Zeman and colleagues. In 2018 we published research showing that fifteen individuals who self-identified as having aphantasia also demonstrated a lack of sensory visual imagery when undergoing the binocular rivalry imagery paradigm, suggesting more than just a metacognitive difference. Here we update these findings with over fifty participants with aphantasia and show that there is evidence for a lack of sensory imagery in aphantasia. How the binocular rivalry paradigm scores relate to the vividness of visual imagery questionnaire (VVIQ) and how aphantasia can be confirmed is discussed.

Comparing two unitisation manipulations: effects on familiarity, recollection-based recognition, and semantic interference.

The recognition of associative memory can be significantly influenced by the use of an encoding strategy known as unitisation, which has been implemented through various manipulations. However, [Shao, H., Opitz, B., Yang, J., & Weng, X. (2016). Recollection reduces unitised familiarity effect. Memory (Hove, England), 24(4), 535-547. https://doi.org/10.1080/09658211.2015.1021258] found intriguing distinctions between two common manipulations, the compound task and the imagery task, leading to a dispute. We propose that differences in levels of processing in the imagery task may account for these discrepancies. This study tested our hypothesis using two approaches. The first two experiments utilised the R/K paradigm to investigate the effects of these methods on familiarity-based and recollection-based recognition. The results demonstrated that familiarity was increased in the compound task, while recollection was increased in the imagery task. In the subsequent two experiments, an interference paradigm was employed to examine differences in semantic processing within the two tasks. The results showed that the compound task did not impact participants' inclination towards lures, while the imagery task led to a bias towards semantic lures over episodic lures, suggesting that the two encodings in the imagery task involve different levels of semantic processing. These results support our hypothesis and underscore the importance of carefully choosing comparisons that account for other variables in the study of unitisation.

Spatial Visual Imagery (SVI)-Based Electroencephalograph Discrimination for Natural CAD Manipulation.

With the increasing demand for natural interactions, people have realized that an intuitive Computer-Aided Design (CAD) interaction mode can reduce the complexity of CAD operation and improve the design experience. Although interaction modes like gaze and gesture are compatible with some complex CAD manipulations, they still require people to express their design intentions physically. The brain contains design intentions implicitly and controls the corresponding body parts that execute the task. Therefore, building an end-to-end channel between the brain and computer as an auxiliary mode for CAD manipulation will allow people to send design intentions mentally and make their interaction more intuitive. This work focuses on the 1-D translation scene and studies a spatial visual imagery (SVI) paradigm to provide theoretical support for building an electroencephalograph (EEG)-based brain-computer interface (BCI) for CAD manipulation. Based on the analysis of three spatial EEG features related to SVI (e.g., common spatial patterns, cross-correlation, and coherence), a multi-feature fusion-based discrimination model was built for SVI. The average accuracy of the intent discrimination of 10 subjects was 86%, and the highest accuracy was 93%. The method proposed was verified to be feasible for discriminating the intentions of CAD object translation with good classification performance. This work further proves the potential of BCI in natural CAD manipulation.

How Integration of a Brain-Machine Interface and Obstacle Detection System Can Improve Wheelchair Control via Movement Imagery.

This study presents a human-computer interaction combined with a brain-machine interface (BMI) and obstacle detection system for remote control of a wheeled robot through movement imagery, providing a potential solution for individuals facing challenges with conventional vehicle operation. The primary focus of this work is the classification of surface EEG signals related to mental activity when envisioning movement and deep relaxation states. Additionally, this work presents a system for obstacle detection based on image processing. The implemented system constitutes a complementary part of the interface. The main contributions of this work include the proposal of a modified 10-20-electrode setup suitable for motor imagery classification, the design of two convolutional neural network (CNNs) models employed to classify signals acquired from sixteen EEG channels, and the implementation of an obstacle detection system based on computer vision integrated with a brain-machine interface. The models developed in this study achieved an accuracy of 83% in classifying EEG signals. The resulting classification outcomes were subsequently utilized to control the movement of a mobile robot. Experimental trials conducted on a designated test track demonstrated real-time control of the robot. The findings indicate the feasibility of integration of the obstacle detection system for collision avoidance with the classification of motor imagery for the purpose of brain-machine interface control of vehicles. The elaborated solution could help paralyzed patients to safely control a wheelchair through EEG and effectively prevent unintended vehicle movements.

Working memory signals in early visual cortex are present in weak and strong imagers.

It has been suggested that visual images are memorized across brief periods of time by vividly imagining them as if they were still there. In line with this, the contents of both working memory and visual imagery are known to be encoded already in early visual cortex. If these signals in early visual areas were indeed to reflect a combined imagery and memory code, one would predict them to be weaker for individuals with reduced visual imagery vividness. Here, we systematically investigated this question in two groups of participants. Strong and weak imagers were asked to remember images across brief delay periods. We were able to reliably reconstruct the memorized stimuli from early visual cortex during the delay. Importantly, in contrast to the prediction, the quality of reconstruction was equally accurate for both strong and weak imagers. The decodable information also closely reflected behavioral precision in both groups, suggesting it could contribute to behavioral performance, even in the extreme case of completely aphantasic individuals. Our data thus suggest that working memory signals in early visual cortex can be present even in the (near) absence of phenomenal imagery.

Neural mechanisms of odour imagery induced by non-figurative visual cues.

Odour imagery, the ability to experience smell when an appropriate stimulus is absent, has widely been documented as being particularly difficult. However, previous studies have shown the beneficial effect of visual cues (e.g., pictures or words) to facilitate performance in numerous tasks of olfactory nature. Therefore, the use of visual cues to evoke odours seems relevant. In this study, our interest is directed towards non-figurative coloured arrangements, which result from a patented technology and aim at chromatically representing any smell from its chemical composition and sensory description. The aim of this study was to characterise the neural mechanisms of odour imagery facilitated by these non-figurative coloured arrangements. Using functional magnetic resonance imaging, we recorded and compared hemodynamic responses during odour imagery facilitated by non-figurative coloured arrangements and pictures. Our findings reveal that the use of non-figurative coloured arrangements during odour imagery solicits olfactory and non-olfactory brain regions (orbitofrontal cortex, insula, hippocampus, thalamus, dorsolateral prefrontal cortex and supplementary motor area), which are mainly involved in olfactory processing and multimodal integration. Moreover, very similar cortical activity was found between the use of non-figurative coloured arrangements and pictures during odour imagery, with increased activity in the supplementary motor area during the use of coloured arrangements only. Overall, non-figurative coloured arrangements could become a robust tool to visually evoke odours without requiring prior familiarity with the depicted odour. Future studies should use psychometric measures to determine the relationships between brain activation, odour imagery ability and vividness of the generated odour images.