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Fully auditory Brain-computer interfaces based on the dichotic listening task (DL-BCIs) are suited for users unable to do any muscular movement, which includes gazing, exploration or coordination of their eyes looking for inputs in form of feedback, stimulation or visual support. However, one of their disadvantages, in contrast with the visual BCIs, is their lower performance that makes them not adequate in applications that require a high accuracy. To overcome this disadvantage, we employed a Bayesian approach in which the DL-BCI was modeled as a Binary phase shift keying receiver for which the accuracy can be estimated a priori as a function of the signal-to-noise ratio. The results showed the measured accuracy to match the predefined target accuracy, thus validating this model that made possible to estimate in advance the classification accuracy on a trial-by-trial basis. This constitutes a novel methodology in the design of fully auditory DL-BCIs that let us first, define the target accuracy for a specific application and second, classify when the signal-to-noise ratio guarantees that target accuracy.

Synthetic sounds, tone-beeps, vowels or syllables are typically used in the assessment of attention to auditory stimuli because they evoke a set of well-known event-related potentials, whose characteristics can be statistically contrasted. Such approach rules out the use of stimuli with non-predictable response, such as human speech. In this study we present a procedure based on the robust binary phase-shift keying (BPSK) receiver that permits the real-time detection of selective attention to human speeches in dichotic listening tasks. The goal was achieved by tagging the speeches with two barely-audible tags whose joined EEG response constitutes a reliable BPSK constellation, which can be detected by means of a BPSK receiver. The results confirmed the expected generation of the BPSK constellation by the human auditory system. Also, the bit-error rate and the information transmission rate achieved in the detection of attention fairly followed the expected curves and equations of the standard BPSK receiver. Actually, it was possible to detect attention as well as the estimation a priori of its accuracy based on the signal-to-noise ratio of the BPSK signals. This procedure, which permits the detection of the attention to human speeches, can be of interest for new potential applications, such as brain–computer interfaces, clinical assessment of the attention in real time or for entertainment.

A scheme to realize all-optical Boolean logic functions, XOR, AND and NAND operations using binary phase shift keyed (BPSK) signal based on quantum-dot semiconductor optical amplifiers (QD-SOA) has been designed and studied. Nonlinear dynamics including carrier heating and spectral hole-burning are taken into account together with the rate equations. We demonstrate XOR, AND and NAND operations at 250Gb/s using a pair of QD-SOA Mach-Zehnder interferometers. Results show that this scheme is suitable for high speed all-optical Boolean logic operations and can improve the output quality comparing with the system using on-off-keyed (OOK) signal.

Chaotic detection of weak signals based on Duffing oscillator uses the property of sensitive dependence on initial conditions (SDIC). A small signal can cause a transition between the states of the system and thus be detected. Different from the early works, we concentrate on using chaotic oscillator as a detector for BPSK signals in very low signal-to-noise ratio (SNR) conditions. Phase transition identification is the key step of weak signals detection by using Duffing oscillator. In this paper, we expose a novel algorithm to use Teager energy operator (TEO) to identify the phase transition, which is more easily to be calculated than the usually used methods. According to this algorithm, a methodology is proposed for detection for BPSK signals using Duffing oscillator. A powerline carrier communication system is studied as an example to illustrate the bit error performance of the proposed chaotic detector. The simulation results show that the proposed detector works much better than the traditional coherent demodulation in strong background noise, and it can improve the error performance of uncoded BPSK signal approaching the Shannon limit curve. The proposed chaotic detector gives us another way to approach the Shannon limit without using any complex channel code technology.