Pablo G. Ortega

Pablo Ortega, Aldo Faisal

We solve the fNIRS left/right hand force decoding problem using a data-driven approach by using a convolutional neural network architecture, the HemCNN. We test HemCNN's decoding capabilities to decode in a streaming way the hand, left or right, from fNIRS data. HemCNN learned to detect which hand executed a grasp at a naturalistic hand action speed of $~1\,$Hz, outperforming standard methods. Since HemCNN does not require baseline correction and the convolution operation is invariant to time translations, our method can help to unlock fNIRS for a variety of real-time tasks. Mobile brain imaging and mobile brain machine interfacing can benefit from this to develop real-world neuroscience and practical human neural interfacing based on BOLD-like signals for the evaluation, assistance and rehabilitation of force generation, such as fusion of fNIRS with EEG signals.

Pablo Ortega, Tong Zhao, Aldo Faisal

Non-invasive cortical neural interfaces have only achieved modest performance in cortical decoding of limb movements and their forces, compared to invasive brain-computer interfaces (BCIs). While non-invasive methodologies are safer, cheaper and vastly more accessible technologies, signals suffer from either poor resolution in the space domain (EEG) or the temporal domain (BOLD signal of functional Near Infrared Spectroscopy, fNIRS). The non-invasive BCI decoding of bimanual force generation and the continuous force signal has not been realised before and so we introduce an isometric grip force tracking task to evaluate the decoding. We find that combining EEG and fNIRS using deep neural networks works better than linear models to decode continuous grip force modulations produced by the left and the right hand. Our multi-modal deep learning decoder achieves 55.2 FVAF[%] in force reconstruction and improves the decoding performance by at least 15% over each individual modality. Our results show a way to achieve continuous hand force decoding using cortical signals obtained with non-invasive mobile brain imaging has immediate impact for rehabilitation, restoration and consumer applications.

Ignacio Espinoza, Marcelo Mendoza, Pablo Ortega et al.

Opinions in forums and social networks are released by millions of people due to the increasing number of users that use Web 2.0 platforms to opine about brands and organizations. For enterprises or government agencies it is almost impossible to track what people say producing a gap between user needs/expectations and organizations actions. To bridge this gap we create Viscovery, a platform for opinion summarization and trend tracking that is able to analyze a stream of opinions recovered from forums. To do this we use dynamic topic models, allowing to uncover the hidden structure of topics behind opinions, characterizing vocabulary dynamics. We extend dynamic topic models for incremental learning, a key aspect needed in Viscovery for model updating in near-real time. In addition, we include in Viscovery sentiment analysis, allowing to separate positive/negative words for a specific topic at different levels of granularity. Viscovery allows to visualize representative opinions and terms in each topic. At a coarse level of granularity, the dynamic of the topics can be analyzed using a 2D topic embedding, suggesting longitudinal topic merging or segmentation. In this paper we report our experience developing this platform, sharing lessons learned and opportunities that arise from the use of sentiment analysis and topic modeling in real world applications.