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Algorithms for locomotion mode recognition (LMR) based on surface electromyography and mechanical sensors have recently been developed and could be used for the neural control of powered prosthetic legs. However, the variations in input signals, caused by physical changes at the sensor interface and human physiological changes, may threaten the reliability of these algorithms. This study aimed to investigate the effectiveness of applying adaptive pattern classifiers for LMR. Three adaptive classifiers, i.e., entropy-based adaptation (EBA), LearnIng From Testing data (LIFT), and Transductive Support Vector Machine (TSVM), were compared and offline evaluated using data collected from two able-bodied subjects and one transfemoral amputee. The offline analysis indicated that the adaptive classifier could effectively maintain or restore the performance of the LMR algorithm when gradual signal variations occurred. EBA and LIFT were recommended because of their better performance and higher computational efficiency. Finally, the EBA was implemented for real-time human-in-the-loop prosthesis control. The online evaluation showed that the applied EBA effectively adapted to changes in input signals across sessions and yielded more reliable prosthesis control over time, compared with the LMR without adaptation. The developed novel adaptive strategy may further enhance the reliability of neurally-controlled prosthetic legs.

Detecting residual consciousness in unresponsive patients is a major clinical concern and a challenge for theoretical neuroscience. To tackle this issue, we recently designed a paradigm that dissociates two electro-encephalographic (EEG) responses to auditory novelty. Whereas a local change in pitch automatically elicits a mismatch negativity (MMN), a change in global sound sequence leads to a late P300b response. The latter component is thought to be present only when subjects consciously perceive the global novelty. Unfortunately, it can be difficult to detect because individual variability is high, especially in clinical recordings. Here, we show that multivariate pattern classifiers can extract subject-specific EEG patterns and predict single-trial local or global novelty responses. We first validate our method with 38 high-density EEG, MEG and intracranial EEG recordings. We empirically demonstrate that our approach circumvents the issues associated with multiple comparisons and individual variability while improving the statistics. Moreover, we confirm in control subjects that local responses are robust to distraction whereas global responses depend on attention. We then investigate 104 vegetative state (VS), minimally conscious state (MCS) and conscious state (CS) patients recorded with high-density EEG. For the local response, the proportion of significant decoding scores (M=60%) does not vary with the state of consciousness. By contrast, for the global response, only 14% of the VS patients' EEG recordings presented a significant effect, compared to 31% in MCS patients' and 52% in CS patients'. In conclusion, single-trial multivariate decoding of novelty responses provides valuable information in non-communicating patients and paves the way towards real-time monitoring of the state of consciousness. •Decoding brain activity may help in detecting consciousness in unresponsive patients.•The Local–Global paradigm separates automatic and consciousness-dependent processes.•The automatic MMN and the attention-dependent P3 can be decoded in single trials.•158 EEG of VS, minimally conscious (MCS) and conscious (CS) patients were analyzed.•Responses to global auditory novelty separate VS patients from CS and MCS patients.

Object perception involves a range of visual and cognitive processes, and is known to include both a feedfoward flow of information from early visual cortical areas to higher cortical areas, along with feedback from areas such as prefrontal cortex. Previous studies have found that low and high spatial frequency information regarding object identity may be processed over different timescales. Here we used the high temporal resolution of magnetoencephalography (MEG) combined with multivariate pattern analysis to measure information specifically related to object identity in peri-frontal and peri-occipital areas. Using stimuli closely matched in their low-level visual content, we found that activity in peri-occipital cortex could be used to decode object identity from ~80ms post stimulus onset, and activity in peri-frontal cortex could also be used to decode object identity from a later time (~265ms post stimulus onset). Low spatial frequency information related to object identity was present in the MEG signal at an earlier time than high spatial frequency information for peri-occipital cortex, but not for peri-frontal cortex. We additionally used Granger causality analysis to compare feedforward and feedback influences on representational content, and found evidence of both an early feedfoward flow and later feedback flow of information related to object identity. We discuss our findings in relation to existing theories of object processing and propose how the methods we use here could be used to address further questions of the neural substrates underlying object perception. [Display omitted] •Pattern analysis in MEG reveals temporal dynamics of object representation.•Granger causal relationships reveal feedfoward and feedback of object representations.•Object identity can be decoded from peri-occipital before peri-frontal sensors.•Object representations show coarse-to-fine evolution in peri-occipital areas.

Empathy relies on the ability to mirror and to explicitly infer others' inner states. Theoretical accounts suggest that memories play a role in empathy, but direct evidence of reactivation of autobiographical memories (AM) in empathy is yet to be shown. We addressed this question in two experiments. In Experiment 1, electrophysiological activity (EEG) was recorded from 28 participants. Participants performed an empathy task in which targets for empathy were depicted in contexts for which participants either did or did not have an AM, followed by a task that explicitly required memory retrieval of the AM and non‐AM contexts. The retrieval task was implemented to extract the neural fingerprints of AM and non‐AM contexts, which were then used to probe data from the empathy task. An EEG pattern classifier was trained and tested across tasks and showed evidence for AM reactivation when participants were preparing their judgement in the empathy task. Participants self‐reported higher empathy for people depicted in situations they had experienced themselves as compared to situations they had not experienced. A second independent fMRI experiment replicated this behavioural finding and showed increased activation for AM compared to non‐AM in the brain networks underlying empathy: precuneus, posterior parietal cortex, superior and inferior parietal lobule, and superior frontal gyrus. Together, our study reports behavioural, electrophysiological, and fMRI evidence that robustly supports AM reactivation in empathy. Theoretical accounts suggest that memories play a role in empathy, but direct evidence of reactivation of autobiographical memories (AM) in empathy is yet to be shown. We addressed this question in one EEG and one fMRI experiment. Participants self‐reported higher empathy for people depicted in situations they had experienced themselves as compared to situations they had not experienced. An EEG pattern classifier showed evidence for AM reactivation when participants were preparing their judgement in the empathy task. Increased activation was observed for AM compared to non‐AM in the brain networks underlying empathy. Together, our study reports behavioural, electrophysiological, and fMRI evidence that robustly supports AM reactivation in empathy.

In this study, a novel approach is proposed for fast prediction of dynamic voltage instability occurrence (as a short term phenomenon and/or a long term one) and voltage stability stiffness of the system, against load disturbances. The main contribution of this paper is in introducing a procedure for generating novel features to be applied to a pattern classifier, by which dynamic voltage stability status of a power system can be predicted. The proposed feature generation procedure only needs measured pre-disturbance variables and disturbance severity provided by phasor measurement units as inputs whereas a set of output variables are derived from an unconstrained power flow program. Since the proposed method does not need any measured post disturbance data, the prediction task can be performed just after the disturbance. Thus, corrective actions can be executed in a short time after the disturbance to inhibit voltage instability. Moreover as no measured post-disturbance data are needed, the proposed method can also be employed in preventive procedures for voltage stability enhancement and/or decreasing possibility of voltage instability occurrence. Training a decision tree based classifier with the proposed features and testing the method on a modified version of Nordic32 test system, the simulation results have demonstrated that the proposed method effectively predicts the status of dynamic voltage stability in the test system.

Class imbalance remains an open problem in pattern recognition, machine learning, and related fields. Many of the state-of-the-art classification algorithms tend to classify all unbalanced dataset patterns by assigning them to a majority class, thus failing to correctly classify a minority class. Associative memories are models used for pattern recall; however, they can also be employed for pattern classification. In this paper, a novel method for improving the classification performance of a hybrid associative classifier with translation (better known by its acronym in Spanish, CHAT) is presented. The extreme center points (ECP) method modifies the CHAT algorithm by exploring alternative vectors in a hyperspace for translating the training data, which is an inherent step of the original algorithm. We demonstrate the importance of our proposal by applying it to imbalanced datasets and comparing the performance to well-known classifiers by means of the balanced accuracy. The proposed method not only enhances the performance of the original CHAT algorithm, but it also outperforms state-of-the-art classifiers in four of the twelve analyzed datasets, making it a suitable algorithm for classification in imbalanced class scenarios.

A new intelligent pattern classifier based on the human being’s thinking logics is developed in this paper, aiming to approximate the optimal design process and avoid the matrix inverse computation in conventional classifier designs. It is seen that the proposed classifier has no parameters to be determined via mathematical optimization. Instead, it is built by using the correlation principles to construct the clusters at first. The middle-level feature vectors can then be extracted from the statistical information of the correlations between the input data and the ones in each pattern cluster. For accurate classification purpose, the advanced feature vectors are generated with the moments’ information of the middle-level feature vectors. After that, Bayesian inference is implemented to make decisions from the weighted sum of the advanced feature components. In addition, a real-time fine-tuning loop (layer) is designed to adaptively “widen” the border of each pattern clustering region such that the input data can be directly classified once they are located in one of the clustering regions. An experiment for the classification of the handwritten digit images from the MNIST database is performed to show the excellent performance and effectiveness of the proposed intelligent pattern classifier.

Ancient manuscripts written in Malay-Arabic characters, which are known as \"Jawi\" characters, are mostly found in Malay world. Nowadays, many of the manuscripts have been digitalized. Unlike Roman letters, there is no optical character recognition (OCR) software for Jawi characters. This article proposes a new algorithm for Jawi character recognition based on Hu’s moment as an invariant feature that we call the tree root (TR) algorithm. The TR algorithm allows every Jawi character to have a unique combination of moment. Seven values of the Hu’s moment are calculated from all Jawi characters, which consist of 36 isolated, 27 initial, 27 middle, and 35 end characters; this makes a total of 125 characters. The TR algorithm was then applied to recognize these characters. To assess the TR algorithm, five characters that had been rotated to 90o and 180o and scaled with factors of 0.5 and 2 were used. Overall, the recognition rate of the TR algorithm was 90.4%; 113 out of 125 characters have a unique combination of moment values, while testing on rotated and scaled characters achieved 82.14% recognition rate. The proposed method showed a superior performance compared with the Support Vector Machine and Euclidian Distance as classifier.

Within the past decade computational approaches adopted from the field of machine learning have provided neuroscientists with powerful new tools for analyzing neural data. For instance, previous studies have applied pattern classification algorithms to electroencephalography data to predict the category of presented visual stimuli, human observer decision choices and task difficulty. Here, we quantitatively compare the ability of pattern classifiers and three ERP metrics (peak amplitude, mean amplitude, and onset latency of the face-selective N170) to predict variations across individuals' behavioral performance in a difficult perceptual task identifying images of faces and cars embedded in noise. We investigate three different pattern classifiers (Classwise Principal Component Analysis, CPCA; Linear Discriminant Analysis, LDA; and Support Vector Machine, SVM), five training methods differing in the selection of training data sets and three analyses procedures for the ERP measures. We show that all three pattern classifier algorithms surpass traditional ERP measurements in their ability to predict individual differences in performance. Although the differences across pattern classifiers were not large, the CPCA method with training data sets restricted to EEG activity for trials in which observers expressed high confidence about their decisions performed the highest at predicting perceptual performance of observers. We also show that the neural activity predicting the performance across individuals was distributed through time starting at 120ms, and unlike the face-selective ERP response, sustained for more than 400ms after stimulus presentation, indicating that both early and late components contain information correlated with observers' behavioral performance. Together, our results further demonstrate the potential of pattern classifiers compared to more traditional ERP techniques as an analysis tool for modeling spatiotemporal dynamics of the human brain and relating neural activity to behavior.

Introduction National estimates suggest that up to 80% of prison inmates meet diagnostic criteria for a substance use disorder. Because more substance abuse treatment while incarcerated is associated with better post‐release outcomes, including a reduced risk of accidental overdose death, the stakes are high in developing novel predictors of substance abuse treatment completion in inmate populations. Methods Using electroencephalography (EEG), this study investigated stimulus‐locked ERP components elicited by distractor stimuli in three tasks (VO‐Distinct, VO‐Repeated, Go/NoGo) as a predictor of treatment discontinuation in a sample of male and female prison inmates. We predicted that those who discontinued treatment early would exhibit a less positive P3a amplitude elicited by distractor stimuli. Results Our predictions regarding ERP components were partially supported. Those who discontinued treatment early exhibited a less positive P3a amplitude and a less positive PC4 in the VO‐D task. In the VO‐R task, however, those who discontinued treatment early exhibited a more negative N200 amplitude rather than the hypothesized less positive P3a amplitude. The discontinuation group also displayed less positive PC4 amplitude. Surprisingly, there were no time‐domain or principle component differences among the groups in the Go/NoGo task. Support Vector Machine (SVM) models of the three tasks accurately classified individuals who discontinued treatment with the best model accurately classifying 75% of inmates. PCA techniques were more sensitive in differentiating groups than the classic time‐domain windowed approach. Conclusions Our pattern of findings are consistent with the context‐updating theory of P300 and may help identify subtypes of ultrahigh‐risk substance abusers who need specialized treatment programs. Using electroencephalography (EEG), this study investigated stimulus‐locked ERP components elicited by distractor stimuli in three tasks (VO‐Distinct, VO‐Repeated, Go/NoGo). Those who discontinued treatment early exhibited a less positive P3a amplitude and a less positive PC4 in the VO‐D task. In the VO‐R task, however, those who discontinued treatment early exhibited a more negative N200 amplitude rather than the hypothesized less positive P3a amplitude. Our pattern of findings is consistent with the context‐updating theory of P300 and may help identify subtypes of ultrahigh‐risk substance abusers who need specialized treatment programs.