A Developmental Neuro-Robotics Approach for Boosting the Recognition of Handwritten Digits

Developmental psychology and neuroimaging research identified a close link between numbers and fingers, which can boost the initial number knowledge in children. Recent evidence shows that a simulation of the children's embodied strategies can improve the machine intelligence too. This article explores the application of embodied strategies to convolutional neural network models in the context of developmental neurorobotics, where the training information is likely to be gradually acquired while operating rather than being abundant and fully available as the classical machine learning scenarios. The experimental analyses show that the proprioceptive information from the robot fingers can improve network accuracy in the recognition of handwritten Arabic digits when training examples and epochs are few. This result is comparable to brain imaging and longitudinal studies with young children. In conclusion, these findings also support the relevance of the embodiment in the case of artificial agents’ training and show a possible way for the humanization of the learning process, where the robotic body can express the internal processes of artificial intelligence making it more understandable for humans

A review of rapid serial visual presentation-based brain-computer interfaces

International audienceRapid serial visual presentation (RSVP) combined with the detection of event related brain responses facilitates the selection of relevant information contained in a stream of images presented rapidly to a human. Event related potentials (ERPs) measured non-invasively with electroencephalography (EEG) can be associated with infrequent targets amongst a stream of images. Human-machine symbiosis may be augmented by enabling human interaction with a computer, without overt movement, and/or enable optimization of image/information sorting processes involving humans. Features of the human visual system impact on the success of the RSVP paradigm, but pre-attentive processing supports the identification of target information post presentation of the information by assessing the co-occurrence or time-locked EEG potentials. This paper presents a comprehensive review and evaluation of the limited but significant literature on research in RSVP-based brain-computer interfaces (BCIs). Applications that use RSVP-based BCIs are categorized based on display mode and protocol design, whilst a range of factors influencing ERP evocation and detection are analyzed. Guidelines for using the RSVP-based BCI paradigms are recommended, with a view to further standardizing methods and enhancing the inter-relatability of experimental design to support future research and the use of RSVP-based BCIs in practice

Developing robust movement decoders for local field potentials

textBrain Computer Interfaces (BCI) are devices that translate acquired neural signals to command and control signals. Applications of BCI include neural rehabilitation and neural prosthesis (thought controlled wheelchair, thought controlled speller etc.) to aid patients with disabilities and to augment human computer interaction. A successful practical BCI requires a faithful acquisition modality to record high quality neural signals; a signal processing system to construct appropriate features from these signals; and an algorithm to translate these features to appropriate outputs. Intracortical recordings like local field potentials provide reliable high SNR signals over long periods and suit BCI applications well. However, the non-stationarity of neural signals poses a challenge in robust decoding of subject behavior. Most BCI research focuses either on developing daily re-calibrated decoders that require exhaustive training sessions; or on providing cross-validation results. Such results ignore the variation of signal characteristics over different sessions and provide an optimistic estimate of BCI performance. Specifically, traditional BCI algorithms fail to perform at the same level on chronological data recordings. Neural signals are susceptible to variations in signal characteristics due to changes in subject behavior and learning, and variability in electrode characteristics due to tissue interactions. While training day-specific BCI overcomes signal variability, BCI re-training causes user frustration and exhaustion. This dissertation presents contributions to solve these challenges in BCI research. Specifically, we developed decoders trained on a single recording session and applied them on subsequently recorded sessions. This strategy evaluates BCI in a practical scenario with a potential to alleviate BCI user frustration without compromising performance. The initial part of the dissertation investigates extracting features that remain robust to changes in neural signal over several days of recordings. It presents a qualitative feature extraction technique based on ranking the instantaneous power of multichannel data. These qualitative features remain robust to outliers and changes in the baseline of neural recordings, while extracting discriminative information. These features form the foundation in developing robust decoders. Next, this dissertation presents a novel algorithm based on the hypothesis that multiple neural spatial patterns describe the variation in behavior. The presented algorithm outperforms the traditional methods in decoding over chronological recordings. Adapting such a decoder over multiple recording sessions (over 6 weeks) provided > 90% accuracy in decoding eight movement directions. In comparison, performance of traditional algorithms like Common Spatial Patterns deteriorates to 16% over the same time. Over time, adaptation reinforces some spatial patterns while diminishing others. Characterizing these spatial patterns reduces model complexity without user input, while retaining the same accuracy levels. Lastly, this dissertation provides an algorithm that overcomes the variation in recording quality. Chronic electrode implantation causes changes in signal-to-noise ratio (SNR) of neural signals. Thus, some signals and their corresponding features available during training become unavailable during testing and vice-versa. The proposed algorithm uses prior knowledge on spatial pattern evolution to estimate unknown neural features. This algorithm overcomes SNR variations and provides up to 93% decoding of eight movement directions over 6 weeks. Since model training requires only one session, this strategy reduces user frustration. In a practical closed-loop BCI, the user learns to produce stable spatial patterns, which improves performance of the proposed algorithms.Electrical and Computer Engineerin

A generic framework for adaptive EEG-based BCI training and operation

International audienceThere are numerous possibilities and motivations for an adaptive BCI, which may not be easy to clarify and organize for a newcomer to the field. To our knowledge, there has not been any work done in classifying the literature on adaptive BCI in a comprehensive and structured way. We propose a conceptual framework, a taxonomy of adaptive BCI methods which encompasses most important approaches to fit them in such a way that a reader can clearly visualize which elements are being adapted and for what reason. In the interest of having a clear review of existing adaptive BCIs, this framework considers adaptation approaches for both the user and the machine, i.e., using instructional design observations as well as the usual machine learning techniques. This framework not only provides a coherent review of such extensive literature but also enables the reader to perceive gaps and flaws in the current BCI systems, which would hopefully bring novel solutions for an overall improvement

Dorsal Anterior Cingulate Cortices Differentially Lateralize Prediction Errors and Outcome Valence in a Decision-Making Task.

The dorsal anterior cingulate cortex (dACC) is proposed to facilitate learning by signaling mismatches between the expected outcome of decisions and the actual outcomes in the form of prediction errors. The dACC is also proposed to discriminate outcome valence—whether a result has positive (either expected or desirable) or negative (either unexpected or undesirable) value. However, direct electrophysiological recordings from human dACC to validate these separate, but integrated, dimensions have not been previously performed. We hypothesized that local field potentials (LFPs) would reveal changes in the dACC related to prediction error and valence and used the unique opportunity offered by deep brain stimulation (DBS) surgery in the dACC of three human subjects to test this hypothesis. We used a cognitive task that involved the presentation of object pairs, a motor response, and audiovisual feedback to guide future object selection choices. The dACC displayed distinctly lateralized theta frequency (3–8 Hz) event-related potential responses—the left hemisphere dACC signaled outcome valence and prediction errors while the right hemisphere dACC was involved in prediction formation. Multivariate analyses provided evidence that the human dACC response to decision outcomes reflects two spatiotemporally distinct early and late systems that are consistent with both our lateralized electrophysiological results and the involvement of the theta frequency oscillatory activity in dACC cognitive processing. Further findings suggested that dACC does not respond to other phases of action-outcome-feedback tasks such as the motor response which supports the notion that dACC primarily signals information that is crucial for behavioral monitoring and not for motor control