Single-channel Conditions Research Articles

Interference Mitigation using Data-driven Sparse Component Analysis

To address the challenge of mitigating asynchronous and non-stationary interference under single-channel conditions, we proposes a sparse component analysis interference mitigation method based on the recurrent neural network. This method aims to recover the desired signal from the received time-frequency over-lapped co-channel signal. Unlike previous interference mitigation methods, our proposed method achieves ”end-to-end” signal recovery in the time domain without any priori requirements on the received signals, making it more universal than existing methods. Numerical results validate the effectiveness of our proposed method and demonstrate its significantly superior mitigation performance compared to existing schemes under different environmental noises, intensities of interfering signals, and generalization test conditions.

A novel end‐to‐end deep separation network based on attention mechanism for single channel blind separation in wireless communication

The traditional methods exhibit unstable performance and high complexity for separating co-frequency modulated wireless communication signals under single-channel conditions. In this study, an end-to-end deep separation network based on the attention mechanism is proposed, which employs the encoder-separator-decoder architecture to implement the separation. The encoder can convert the signal into a high-dimensional feature representation for the separator to achieve separation of the signal in a high-dimensional space. The core of the proposed deep separation network is the innovative separator, which is mainly composed of attention-based convolution units with residual connection. The attention-based convolution unit integrates the large kernel convolution and modified global context (GC) block to simultaneously capture the local and global information of the signal. Furthermore, we improve the GC block to implement channel weighting and location weighting for the given feature map, thus further enhancing the adaptability of the model. The experimental results show that the proposed method not only outperforms the traditional methods in separating mixed signals with the same modulation, but also enables the separation of mixed signals with different modulations.

Neural Spectrospatial Filtering

As the most widely-used spatial filtering approach for multi-channel speech separation, beamforming extracts the target speech signal arriving from a specific direction. An emerging alternative approach is multi-channel complex spectral mapping, which trains a deep neural network (DNN) to directly estimate the real and imaginary spectrograms of the target speech signal from those of the multi-channel noisy mixture. In this all-neural approach, the trained DNN itself becomes a nonlinear, time-varying spectrospatial filter. However, it remains unclear how this approach performs relative to commonly-used beamforming techniques on different array configurations and acoustic environments. This paper is devoted to examining this issue in a systematic way. Comprehensive evaluations show that multi-channel complex spectral mapping achieves separation performance comparable to or better than beamforming for different array geometries and speech separation tasks and reduces to monaural complex spectral mapping in single-channel conditions, demonstrating the general utility of this approach on multi-channel and single-channel speech separation. In addition, such an approach is computationally more efficient than widely-used mask-based beamforming. We conclude that this neural spectrospatial filter provides a strong alternative to traditional and mask-based beamforming.

Influence of the Number of Channels and Classification Algorithm on the Performance Robustness to Electrode Shift in Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces.

There remains an active investigation on elevating the classification accuracy and information transfer rate of brain-computer interfaces based on steady-state visual evoked potential. However, it has often been ignored that the performance of steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) can be affected through the minor displacement of the electrodes from their optimal locations in practical applications because of the mislocation of electrodes and/or concurrent use of electroencephalography (EEG) devices with external devices, such as virtual reality headsets. In this study, we evaluated the performance robustness of SSVEP-based BCIs with respect to the changes in electrode locations for various channel configurations and classification algorithms. Our experiments involved 21 participants, where EEG signals were recorded from the scalp electrodes densely attached to the occipital area of the participants. The classification accuracies for all the possible cases of electrode location shifts for various channel configurations (1–3 channels) were calculated using five training-free SSVEP classification algorithms, i.e., the canonical correlation analysis (CCA), extended CCA, filter bank CCA, multivariate synchronization index (MSI), and extended MSI (EMSI). Then, the performances of the BCIs were evaluated using two measures, i.e., the average classification accuracy (ACA) across the electrode shifts and robustness to the electrode shift (RES). Our results showed that the ACA increased with an increase in the number of channels regardless of the algorithm. However, the RES was enhanced with an increase in the number of channels only when MSI and EMSI were employed. While both ACA and RES values for the five algorithms were similar under the single-channel condition, both ACA and RES values for MSI and EMSI were higher than those of the other algorithms under the multichannel (i.e., two or three electrodes) conditions. In addition, EMSI outperformed MSI when comparing the ACA and RES values under the multichannel conditions. In conclusion, our results suggested that the use of multichannel configuration and employment of EMSI could make the performance of SSVEP-based BCIs more robust to the electrode shift from the optimal locations.

Muscle Artifact Removal Toward Mobile SSVEP-Based BCI: A Comparative Study

Steady-state visual evoked potential (SSVEP) serves as one of the extensively utilized paradigms for brain–computer interface (BCI). SSVEP-based BCI has advantages of good classification accuracy, high information transfer rate (ITR), and little user training. However, there exist few studies investigating SSVEP-based BCI in the mobile situation, where electroencephalography (EEG) data are inevitably contaminated by muscle activities, and meanwhile, a limited number of channels are preferred. In this study, two major muscle artifact removal schemes are investigated in the dynamic scenario. One scheme adopts state-of-the-art blind source separation (BSS) methods without electromyogram (EMG) reference, while the other utilizes auxiliary EMG channels as a reference in the denoising process. To evaluate the performance of two schemes, we have tested them on real EEG data collected from ten subjects. The results demonstrate that, in the few-channel condition of SSVEP data, there are classification accuracy improvements of 9.93% for the canonical correlation analysis (CCA) indicator and 15.72% for the task-related component analysis (TRCA) indicator after processed by recursive least-squares (RLS) filtering with auxiliary EMG, and in the single-channel condition, there are classification accuracy improvements of 22.00% for the CCA indicator and 24.42% for the TRCA indicator after RLS filtering, averaged across all the subjects and data lengths. However, the BSS-based methods fail to improve the classification accuracies of SSVEP. Thus, it is of necessity to mount additional EMG channels for effective muscle activity suppression. This study provides an insightful suggestion for future real-life BCI applications.

Transient responses to slowly varying input waveforms in backward pumped Raman amplifiers

In this article, a novel numerical algorithm to simulate power transient in backward pumped Raman amplifiers is proposed. Based on this algorithm, transient response to a slowly varying input power waveform is investigated for the first time. We find that a properly designed, slowly varied input leading edge can reduce the output up-shoot drastically in a single-channel condition, while in a multi-channel condition, a gradually changed input waveform can eliminate the abrupt power up-shoot due to forward signal-to-signal Raman interactions and obtain a better gain clamping result by using pump adjustment. These conclusions will play a role in Raman amplified optical burst mode switching (or packet switching) systems and DWDM networks.

Simultaneous two-channel signal detection. II. Correlated and uncorrelated signals.

Two-channel auditory signal detection was investigated with narrow-band noise signals masked by independent, binaurally uncorrelated noise. The two-channel tasks included conditions where the signals in each channel were correlated, uncorrelated, or opposite in phase. No significant differences were observed between the single-channel conditions and the two-channel correlated signal condition, but performance in the uncorrelated and 180° phase conditions was significantly poorer. A comprehensive analysis of cross-channel interaction indicated a cross-channel inhibitory or masking effect, which may be counteracted by the presence of lateralization cues. Constraints on signal detectability due to a limited processing capacity appeared to be negligible.