Restricted Minimum Error Entropy Criterion for Robust Classification

The minimum error entropy (MEE) criterion has been verified as a powerful approach for non-Gaussian signal processing and robust machine learning. However, the implementation of MEE on robust classification is rather a vacancy in the literature. The original MEE only focuses on minimizing the Renyi's quadratic entropy of the error probability distribution function (PDF), which could cause failure in noisy classification tasks. To this end, we analyze the optimal error distribution in the presence of outliers for those classifiers with continuous errors, and introduce a simple codebook to restrict MEE so that it drives the error PDF towards the desired case. Half-quadratic based optimization and convergence analysis of the new learning criterion, called restricted MEE (RMEE), are provided. Experimental results with logistic regression and extreme learning machine are presented to verify the desirable robustness of RMEE

Partial Maximum Correntropy Regression for Robust Trajectory Decoding from Noisy Epidural Electrocorticographic Signals

The Partial Least Square Regression (PLSR) exhibits admirable competence for predicting continuous variables from inter-correlated brain recordings in the brain-computer interface. However, PLSR is in essence formulated based on the least square criterion, thus, being non-robust with respect to noises. The aim of this study is to propose a new robust implementation for PLSR. To this end, the maximum correntropy criterion (MCC) is used to propose a new robust variant of PLSR, called as Partial Maximum Correntropy Regression (PMCR). The half-quadratic optimization is utilized to calculate the robust projectors for the dimensionality reduction, and the regression coefficients are optimized by a fixed-point approach. We evaluate the proposed PMCR with a synthetic example and the public Neurotycho electrocorticography (ECoG) datasets. The extensive experimental results demonstrate that, the proposed PMCR can achieve better prediction performance than the conventional PLSR and existing variants with three different performance indicators in high-dimensional and noisy regression tasks. PMCR can suppress the performance degradation caused by the adverse noise, ameliorating the decoding robustness of the brain-computer interface

EMG space similarity feedback promotes learning of expert-like muscle activation patterns in a complex motor skill

Augmented feedback provided by a coach or augmented reality system can facilitate the acquisition of a motor skill. Verbal instructions and visual aids can be effective in providing feedback about the kinematics of the desired movements. However, many skills require mastering not only kinematic, but also complex kinetic patterns, for which feedback is harder to convey. Here, we propose the electromyography (EMG) space similarity feedback, which may indirectly convey kinematic and kinetic feedback by comparing the muscle activations of the learner and an expert in the task. The EMG space similarity feedback is a score that reflects how well a set of muscle synergies extracted from the expert can reconstruct the learner’s EMG when performing the task. We tested the EMG space similarity feedback in a virtual bimanual polishing task that uses a robotic system to simulate the dynamics of a real polishing operation. We measured the expert’s and learner’s EMG from eight muscles in each arm during the real and virtual polishing tasks, respectively. The goal of the virtual task was to smoothen the surface of a virtual object. Therefore, we defined performance in the task as the smoothness of the object at the end of a trial. We separated learners into real feedback and null feedback groups to assess the effects of the EMG space similarity feedback. The real and null feedback groups received veridic and no EMG space similarity feedback, respectively. Subjects participated in five training sessions on different days, and we evaluated their performance on each day. Subjects in both groups were able to increase smoothness throughout the training sessions, with no significant differences between groups. However, subjects in the real feedback group were able to improve in the EMG space similarity score to a significantly greater extent than the null feedback group. Additionally, subjects in the real feedback group produced muscle activations that became increasingly consistent with an important muscle synergy found in the expert. Our results indicate that the EMG space similarity feedback promotes acquiring expert-like muscle activation patterns, suggesting that it may assist in the acquisition of complex motor skills

A Biased Bayesian Inference for Decision-Making and Cognitive Control

Although classical decision-making studies have assumed that subjects behave in a Bayes-optimal way, the sub-optimality that causes biases in decision-making is currently under debate. Here, we propose a synthesis based on exponentially-biased Bayesian inference, including various decision-making and probability judgments with different bias levels. We arrange three major parameter estimation methods in a two-dimensional bias parameter space (prior and likelihood), of the biased Bayesian inference. Then, we discuss a neural implementation of the biased Bayesian inference on the basis of changes in weights in neural connections, which we regarded as a combination of leaky/unstable neural integrator and probabilistic population coding. Finally, we discuss mechanisms of cognitive control which may regulate the bias levels

Versatile Locomotion Control of a Hexapod Robot Using a Hierarchical Network of Nonlinear Oscillator Circuits

A novel hierarchical network based on coupled nonlinear oscillators is proposed for motor pattern generation in hexapod robots. Its architecture consists of a central pattern generator (CPG), producing the global leg coordination pattern, coupled with six local pattern generators, each devoted to generating the trajectory of one leg. Every node comprises a simple nonlinear oscillator and is well-suited for implementation in a standard field-programmable analog array device. The network enables versatile locomotion control based on five high-level parameters which determine the inter-oscillator coupling pattern via simple rules. The controller was realized on dedicated hardware, deployed to control an ant-like hexapod robot, and multi-sensory telemetry was performed. As a function of a single parameter, it was able to stably reproduce the canonical gaits observed in six-legged insects, namely the wave, tetrapod, and tripod gaits. A second parameter enabled driving the robot in ant-like and cockroach-like postures. Three further parameters enabled inhibiting and resuming walking, steering, and producing uncoordinated movement. Emergent phenomena were observed in the form of a multitude of intermediate gaits and of hysteresis and metastability close to a point of gait transition. The primary contributions of this paper reside in the hierarchical controller architecture and associated approach for collapsing a large set of low-level parameters, stemming from the complex hexapod kinematics, into only five high-level parameters. Such parameters can be changed dynamically, an aspect of broad practical relevance opening new avenues for driving hexapod robots via afferent signals from other circuits representing higher brain areas, or by means of suitable brain-computer interfaces. An additional contribution is the detailed characterization via telemetry of the physical robot, involving the definition of parameters which may aid future comparison with other controllers. The present results renew interest into analog CPG architectures and reinforce the generality of the connectionist approach

Classification of Movement Intention Using Independent Components of Premovement EEG

Many previous studies on brain-machine interfaces (BMIs) have focused on electroencephalography (EEG) signals elicited during motor-command execution to generate device commands. However, exploiting pre-execution brain activity related to movement intention could improve the practical applicability of BMIs. Therefore, in this study we investigated whether EEG signals occurring before movement execution could be used to classify movement intention. Six subjects performed reaching tasks that required them to move a cursor to one of four targets distributed horizontally and vertically from the center. Using independent components of EEG acquired during a premovement phase, two-class classifications were performed for left vs. right trials and top vs. bottom trials using a support vector machine. Instructions were presented visually (test) and aurally (condition). In the test condition, accuracy for a single window was about 75%, and it increased to 85% in classification using two windows. In the control condition, accuracy for a single window was about 73%, and it increased to 80% in classification using two windows. Classification results showed that a combination of two windows from different time intervals during the premovement phase improved classification performance in the both conditions compared to a single window classification. By categorizing the independent components according to spatial pattern, we found that information depending on the modality can improve classification performance. We confirmed that EEG signals occurring during movement preparation can be used to control a BMI

A Decision-Theoretic Model of Behavior Change

Undesirable habitual or addictive behaviors are often difficult to change. The issue of “behavior change” has long been studied in various research fields. Several models for behavior change have converged to the hypothesis that attitudes, norms, and self-efficacy are important determinants of intentions and behavior. To improve the accuracy of behavior-change models, some researchers have tried to combine behavioral economics models with existing models for behavior change. However, these attempts have failed because the existing models [e.g., Theory of Planned Behavior (TPB)] are not consistent with Expected Utility Theory (EUT), which underlies various behavioral economics models. In the present paper, we clarify the corresponding components between existing models for behavior change and EUT, and propose a new model, the Decision-Theoretic Model of behavior change (DTM), which is a natural extension of ordinary EUT

Differential expression of nuclear lamin subtypes in the neural cells of the adult rat cerebral cortex

Lamins are type V intermediate filament proteins that are located beneath the inner nuclear membrane. In mammalian somatic cells, LMNB1 and LMNB2 encode somatic lamins B1 and B2, respectively, and the LMNA gene is alternatively spliced to generate somatic lamins A and C. Mutations in lamin genes have been linked to many human hereditary diseases, including neurodegenerative disorders. Knowledge about lamins in the nervous system has been accumulated recently, but a precise analysis of lamin subtypes in glial cells has not yet been reported. In this study we investigated the composition of lamin subtypes in neurons, astrocytes, oligodendrocyte-lineage cells, and microglia in the adult rat cerebral cortex using an immunohistochemical staining method. Lamin A was not observed in neurons and glial cells. Lamin C was observed in astrocytes, mature oligodendrocytes and neurons, but not observed in oligodendrocyte progenitor cells. Microglia also did not stain positive for lamin C which differed from macrophages, with lamin C positive. Lamin B1 and B2 were observed in all glial cells and neurons. Lamin B1 was intensely positive in oligodendrocyte progenitor cells compared with other glial cells and neurons. Lamin B2 was weakly positive in all glial cells compared to neurons. Our current study might provide useful information to reveal how the onset mechanisms of human neurodegenerative diseases are associated with mutations in genes for nuclear lamin proteins