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Brain-computer interfaces (BCI) are using the EEG (Electroencephalogram), the ECoG (Electrocorticogram) and trains of action potentials as inputs to analyze brain activity for communication purposes and/or the control of external devices. Thus far it is not known whether a BCI system can be developed that utilizes the states of brain structures that are situated well below the cortical surface, such as the hippocampus. In order to address this question we used the activity of hippocampal place cells to predict the position of an rodent in real-time. First, spike activity was recorded from the hippocampus during foraging and analyzed off-line to optimize the spike sorting and position reconstruction algorithm of rats. Then the spike activity was recorded and analyzed in real-time. The rat was running in a box of 80x80 cm and its locomotor movement was captured with a video tracking system. Data were acquired to calculate the rat’s trajectories and to identify place fields. Then a Bayesian classifier was trained to predict the position of the rat given its neural activity. This information was used in subsequent trials to predict the rat’s position in real-time.The real-time experiments were successfully performed and yielded an error between 12.2 and 17.4 % using 5-6 neurons. It must be noted here that the encoding step was done with data recorded before the real-time experiment and comparable accuracies between off-line (mean error of 15.9 % for 3 rats) and real-time experiments (mean error of 14.7%) were achieved. The experiment shows proof of principle that position reconstruction can be done in real-time, that place cells were stable and spike sorting was robust enough to generalize from the training run to the real-time reconstruction phase of the experiment. Real-time reconstruction may be used for a variety of purposes, including creating behavioral-neuronal feedback loops or for implementing neuroprosthetic control.

We discuss three experiments that investigate how virtual limbs and bodies can come to feel like real limbs and bodies. The first experiment shows that an illusion of ownership of a virtual arm appearing to project out of a person’s shoulder can be produced by tactile stimulation on a person’s hidden real hand and synchronous stimulation on the seen virtual hand. The second shows that the illusion can be produced by synchronous movement of the person’s hidden real hand and a virtual hand. The third shows that a weaker form of the illusion can be produced when a brain-computer interface is employed to move the virtual hand by means of motor imagery without any tactile stimulation. We discuss related studies that indicate that the ownership illusion may be generated for an entire body. This has important implications for the scientific understanding of body ownership and several practical applications.

Developing new standardized tools to characterize brain recording devices is critical to evaluate neural probes and for translation to clinical use. The signal-to-noise ratio (SNR) measurement is the gold standard for quantifying the performance of brain recording devices. Given the drawbacks with the SNR measure, our first objective was to devise a new method to calculate the SNR of neural signals to distinguish signal from noise. Our second objective was to apply this new SNR method to evaluate electrodes of three different materials (platinum black, Pt; carbon nanotubes, CNTs; and gold, Au) co-localized in tritrodes to record from the same cortical area using specifically designed multielectrode arrays. Hence, we devised an approach to calculate SNR at different frequencies based on the features of cortical slow oscillations (SO). Since SO consist in the alternation of silent periods (Down states) and active periods (Up states) of neuronal activity, we used these as noise and signal, respectively. The spectral SNR was computed as the power spectral density (PSD) of Up states (signal) divided by the PSD of Down states (noise). We found that Pt and CNTs electrodes have better recording performance than Au electrodes for the explored frequency range (5–1500 Hz). Together with two proposed SNR estimators for the lower and upper frequency limits, these results substantiate our SNR calculation at different frequency bands. Our results provide a new validated SNR measure that provides rich information of the performance of recording devices at different brain activity frequency bands (