Adaptive neural network classifier for decoding finger movements
While non-invasive Brain-to-Computer interface can accurately classify the lateralization of hand moments, the distinction of fingers activation in the same hand is limited by their local and overlapping representation in the motor cortex. In particular, the low signal-to-noise ratio restrains the opportunity to identify meaningful patterns in a supervised fashion. Here we combined Magnetoencephalography (MEG) recordings with advanced decoding strategy to classify finger movements at single trial level. We recorded eight subjects performing a serial reaction time task, where they pressed four buttons with left and right index and middle fingers. We evaluated the classification performance of hand and finger movements with increasingly complex approaches: supervised common spatial patterns and logistic regression (CSP + LR) and unsupervised linear finite convolutional neural network (LF-CNN). The right vs left fingers classification performance was accurate above 90% for all methods. However, the classification of the single finger provided the following accuracy: CSP+SVM : – 68 ± 7%, LF-CNN : 71 ± 10%. CNN methods allowed the inspection of spatial and spectral patterns, which reflected activity in the motor cortex in the theta and alpha ranges. Thus, we have shown that the use of CNN in decoding MEG single trials with low signal to noise ratio is a promising approach that, in turn, could be extended to a manifold of problems in clinical and cognitive neuroscience.
Time as a continuous dimension in natural and artificial networks
Neural representations of time are central to our understanding of the world around us. I review cognitive, neurophysiological and theoretical work that converges on three simple ideas. First, the time of past events is remembered via populations of neurons with a continuum of functional time constants. Second, these time constants evenly tile the log time axis. This results in a neural Weber-Fechner scale for time which can support behavioral Weber-Fechner laws and characteristic behavioral effects in memory experiments. Third, these populations appear as dual pairs---one type of population contains cells that change firing rate monotonically over time and a second type of population that has circumscribed temporal receptive fields. These ideas can be used to build artificial neural networks that have novel properties. Of particular interest, a convolutional neural network built using these principles can generalize to arbitrary rescaling of its inputs. That is, after learning to perform a classification task on a time series presented at one speed, it successfully classifies stimuli presented slowed down or sped up. This result illustrates the point that this confluence of ideas originating in cognitive psychology and measured in the mammalian brain could have wide-reaching impacts on AI research.