Objective. Brain–computer interfaces (BCIs) translate neural activity into control signals for assistive devices in order to help people with motor disabilities communicate effectively. In this work, we introduce a new BCI architecture that improves control of a BCI computer cursor to type on a virtual keyboard. Approach. Our BCI architecture incorporates an external artificial intelligence (AI) that beneficially augments the movement trajectories of the BCI. This AI-BCI leverages past user actions, at both long (100 s of seconds ago) and short (100 s of milliseconds ago) timescales, to modify the BCI’s trajectories. Main results. We tested our AI-BCI in a closed-loop BCI simulator with nine human subjects performing a typing task. We demonstrate that our AI-BCI achieves: (1) categorically higher information communication rates, (2) quicker ballistic movements between targets, (3) improved precision control to ‘dial in’ on targets, and (4) more efficient movement trajectories. We further show that our AI-BCI increases performance across a wide control quality spectrum from poor to proficient control. Significance. This AI-BCI architecture, by increasing BCI performance across all key metrics evaluated, may increase the clinical viability of BCI systems.

Automatic modulation classification is a challenging problem with multiple applications including cognitive radio and signals intelligence. Most of the existing efforts to solve this problem are only applicable when the signal to noise ratio (SNR) is high and/or long observations of the signal are available. Recent work has focused on applying shallow and deep machine learning (ML) to this problem. In this paper, we present an exploration of such deep learning and ensemble learning techniques that was used to win the Army Rapid Capability Office (RCO) 2018 Signal Classification Challenge. An expert feature extraction and shallow learning approach is discussed in a simultaneous publication. We evaluated multiple state-of-the-art deep learning network architectures and adapted them to work in the RF signal domain instead of the image/computer-vision domain. The best deep learning methods were merged with the best expert feature extraction and shallow learning methods using ensemble learning. Finally, the ensemble classifier was calibrated to obtain marginal gains. The methods discussed are capable of correctly classifying waveforms at-10 dB SNR with over 63% accuracy and signals at +10 dB SNR with over 95% accuracy from an Army RCO provided training set.

Automatic modulation classification is a challenging problem with multiple applications including cognitive radio and signals intelligence. Most of the existing efforts to solve this problem are only applicable when the signal to noise ratio (SNR) is high and/or long observations of the signal are available. Recent work has focused on applying shallow and deep machine learning (ML) to this problem. Feature generation, where raw signal information is transformed prior to attempting classification is a key part of this process. A big question that researchers face is whether to let the deep learning system infer the relevant features or build expert features based on expected signal characteristics. In this paper, we present novel signal feature extraction methods for use in signal classification via ML. The deep learning and combined approaches are discussed in a simultaneous publication. Expert features were utilized via ensemble leaning and shallow neural networks to win the Army Rapid Capability Office (RCO) 2018 Signal Classification Challenge. The features include both standard statistical measurements such as variance and kurtosis, as well as measurements tailored for specific waveform families. We discuss the best statistical descriptors along with a ranked list of signal features and discuss individual feature importance. We then demonstrate our implementation of these features and discuss effectiveness in estimating different modulation classes. The methods discussed when combined with deep learning are capable of correctly classifying waveforms at-10 dB SNR with over 63% accuracy and signals at +10 dB SNR with over 95% accuracy from an Army RCO provided training set.

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