Decoding covert somatosensory attention by a BCI system calibrated with tactile sensation

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Objective: We propose a novel calibration strategy to facilitate the decoding of covert somatosensory attention by exploring the oscillatory dynamics induced by tactile sensation. Methods: It was hypothesized that the similarity of the oscillatory pattern between stimulation sensation (SS, real sensation) and somatosensory attentional orientation (SAO) provides a way to decode covert somatic attention. Subjects were instructed to sense the tactile stimulation, which was applied to the left (SS-L) or the right (SS-R) wrist. The BCI system was calibrated with the sensation data and then applied for online SAO decoding. Results: Both SS and SAO showed oscillatory activation concentrated on the contralateral somatosensory hemisphere. Offline analysis showed that the proposed calibration method led to greater accuracy than the traditional calibration method based on SAO only. This is confirmed by online experiments, where the online accuracy on 15 subjects was 78.8±13.1%, with 12 subjects >70% and 4 subject >90%. Conclusion: By integrating the stimulus-induced oscillatory dynamics from sensory cortex, covert somatosensory attention can be reliably decoded by a BCI system calibrated with tactile sensation. Significance: Indeed, real tactile sensation is more consistent during calibration than SAO. This brain-computer interfacing approach may find application for stroke and completely locked-in patients with preserved somatic sensation.University Starter Grant of the University of Waterloo (No. 203859) National Natural Science Foundation of China (Grant No. 51620105002

Fast Recognition of BCI-Inefficient Users Using Physiological Features from EEG Signals: A Screening Study of Stroke Patients

Motor imagery (MI) based brain-computer interface (BCI) has been developed as an alternative therapy for stroke rehabilitation. However, experimental evidence demonstrates that a significant portion (10% to 50%) of subjects are BCI-illiterate users (accuracy less than 70%). Thus, predicting BCI performance prior to clinical BCI usage would facilitate the selection of suitable end-users and improve the efficiency of stroke rehabilitation. In the current study, we proposed two physiological variables, i.e., laterality index (LI) and cortical activation strength (CAS), to predict MI-BCI performance. Twenty-four stroke patients and ten healthy subjects were recruited for this study. Each subject was required to perform two blocks of left- and right-hand MI tasks. Linear regression analyses were performed between the BCI accuracies and two physiological predictors. Here, the predictors were calculated from the electroencephalography (EEG) signals during paretic hand MI tasks (5 trials; approximately one minute). LI values exhibited a statistically significant correlation with two-class BCI (left vs. right) performance (r=-0.732, p<0.001), and CAS values exhibited a statistically significant correlation with brain-switch BCI (task vs. idle) performance (r=0.641, p<0.001). Furthermore, the BCI-illiterate users were successfully recognized with a sensitivity of 88.2% and a specificity of 85.7% in the two-class BCI. The brain-switch BCI achieved a sensitivity of 100.0% and a specificity of 87.5% in the discrimination of BCI-illiterate users. These results demonstrated that the proposed BCI predictors were promising to promote the BCI usage in stroke rehabilitation and contribute to a better understanding of the BCI-illiteracy phenomenon in stroke patients.National Natural Science Foundation of China (Grant No. 51620105002) National High Technology Research and Development Program (863 Program) of China (Grant No.2015AA020501

Applying the driver-pressure-state-impact-response model to ecological restoration: A case study of comprehensive zoning and benefit assessment in Zhejiang Province, China

Ecosystem degradation is a global problem that poses a significant threat to the sustainable development of human societies, particularly in developing countries, such as China. In response, China has implemented a series of ecological restoration (ER) policies over recent years. However, significant regional developmental disparities, pronounced spatial heterogeneity of ecological issues, and substantial historical debt for ER in China present considerable obstacles and financial burdens to the effective implementation of ER strategies. Delineating ER zones and assessing the ER benefits are essential for developing effective ER strategies and implementing ER projects. In this study, we constructed a comprehensive framework for ER utilizing the Driver-Pressure-State-Impact-Response (DPSIR) model, quantified the urbanization levels, ecological state, and restoration costs in Zhejiang Province to delineate ER zones, integrated the patch-generating land use simulation model with the ecosystem service value assessment method to quantify the benefits of ER, and ultimately developed tailored ER strategies. The results showed that: (1) The pattern of urbanization levels was characterized by high levels in the northeast and low levels in the southwest, which constrated with the ecological state. The areas of high restoration costs were located in the northeastern and southeastern regions, and the areas of low restoration costs were situated in the southwestern region. (2) The rate of construction land expansion is significantly curtailed under the ER scenario compared to the natural development scenario in 2035, while forest areas have seen effective protection and an increase from the levels of 2020. (3) The ER policy is projected to generate ecological benefits totaling CNY 8.23 billion by 2035, substantially reducing the rate of ecosystem degradation. (4) Zhejiang Province is divided into five zones at the county scale: ecological autonomous protection zone, ecological core protection zone, ecological priority restoration zone, ecological control zone, and moderate development zone. Strategies have been devised based on the forecasted benefits of ER, offering valuable insights into ecological management. These findings aim to enhance the understanding of ER and support the development and implementation of regional ecological policies

Enhancing Few-shot CLIP with Semantic-Aware Fine-Tuning

Learning generalized representations from limited training samples is crucial for applying deep neural networks in low-resource scenarios. Recently, methods based on Contrastive Language-Image Pre-training (CLIP) have exhibited promising performance in few-shot adaptation tasks. To avoid catastrophic forgetting and overfitting caused by few-shot fine-tuning, existing works usually freeze the parameters of CLIP pre-trained on large-scale datasets, overlooking the possibility that some parameters might not be suitable for downstream tasks. To this end, we revisit CLIP's visual encoder with a specific focus on its distinctive attention pooling layer, which performs a spatial weighted-sum of the dense feature maps. Given that dense feature maps contain meaningful semantic information, and different semantics hold varying importance for diverse downstream tasks (such as prioritizing semantics like ears and eyes in pet classification tasks rather than side mirrors), using the same weighted-sum operation for dense features across different few-shot tasks might not be appropriate. Hence, we propose fine-tuning the parameters of the attention pooling layer during the training process to encourage the model to focus on task-specific semantics. In the inference process, we perform residual blending between the features pooled by the fine-tuned and the original attention pooling layers to incorporate both the few-shot knowledge and the pre-trained CLIP's prior knowledge. We term this method as Semantic-Aware FinE-tuning (SAFE). SAFE is effective in enhancing the conventional few-shot CLIP and is compatible with the existing adapter approach (termed SAFE-A)

Historical context modifies plant diversity–community productivity relationships in alpine grassland

While most studies yield positive relationships between biodiversity (B) and ecosystem functioning (EF), awareness is growing that BEF relationships can vary with ecological context. The awareness has led to increased efforts to understand how contemporary environmental context modifies BEF relationships, but the role of historical context, and the mechanisms by which it may influence biodiversity effects, remains poorly understood. We examined how historical context alters plant diversity–community productivity relationships via plant species interactions in alpine grassland. We also tested how historical context modifies interactions between plants and arbuscular mycorrhizal (AM) fungi, which can potentially mediate the above processes. We studied biodiversity effects on plant community productivity at two grassland sites with different histories related to grazing intensity—heavy versus light livestock grazing—but similar current management. We assembled experimental communities of identical species composition with plants from each of the two sites in disturbed soil from a contemporary heavily grazed grassland, ranging in species richness from one to two, three and six species. Moreover, we carried out a mycorrhizal hyphae-exclusion experiment to test how plant interactions with AM fungi influence plant responses to historical context. We detected a significantly positive diversity–productivity relationship that was driven by complementarity effects in communities composed of plants from the site without heavy-grazing history, but no such relationship in plant communities composed of plants from the site with heavy-grazing history. Plants from the site with heavy-grazing history had increased competitive ability and increased yields in low-diversity communities but disrupted complementarity effects in high-diversity communities. Moreover, plants of one species from the site with heavy-grazing history benefitted more from AM fungal communities than did plants from the site without such history. Synthesis. Using the same experimental design and species, communities assembled by plants from two sites with different historical contexts showed different plant diversity–community productivity relationships. Our results suggest that historical context can alter plant diversity–community productivity relationships via plant species interactions and potentially plant–soil interactions. Therefore, considering historical contexts of ecological communities is of importance for advancing our understanding of long-term impacts of anthropogenic disturbance on ecosystem functioning