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.

Origami-Based Haptic Syringe for Local Anesthesia Simulator.

Although medical simulators have benefited from the use of haptics and virtual reality (VR) for decades, the former has become the bottleneck in producing a low-cost, compact, and accurate training experience. This is particularly the case for the inferior alveolar nerve block (IANB) procedure in dentistry, which is one of the most difficult motor skills to acquire. As existing works are still oversimplified or overcomplicated for practical deployment, we introduce an origami-based haptic syringe interface for IANB local anesthesia training. By harnessing the versatile mechanical tunability of the Kresling origami pattern, our interface simulated the tactile experience of the plunger while injecting the anesthetic solution. We present the design, development, and characterization process, as well as a preliminary usability study. The force profile generated by the syringe interface is perceptually similar with that of the Carpule syringe. The usability study suggests that the haptic syringe significantly improves the IANB training simulation and its potential to be utilized in several other medical training/simulation applications.