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Advances in physics

Objective. Signal classification is an important issue in brain computer interface (BCI) systems. Deep learning approaches have been used successfully in many recent studies to learn features and classify different types of data. However, the number of studies that employ these approaches on BCI applications is very limited. In this study we aim to use deep learning methods to improve classification performance of EEG motor imagery signals. Approach. In this study we investigate convolutional neural networks (CNN) and stacked autoencoders (SAE) to classify EEG Motor Imagery signals. A new form of input is introduced to combine time, frequency and location information extracted from EEG signal and it is used in CNN having one 1D convolutional and one max-pooling layers. We also proposed a new deep network by combining CNN and SAE. In this network, the features that are extracted in CNN are classified through the deep network SAE. Main results. The classification performance obtained by the proposed method on BCI competition IV dataset 2b in terms of kappa value is 0.547. Our approach yields 9% improvement over the winner algorithm of the competition. Significance. Our results show that deep learning methods provide better classification performance compared to other state of art approaches. These methods can be applied successfully to BCI systems where the amount of data is large due to daily recording.

https://iopscience.iop.org/article/10.1088/1741-2560/14/1/016003/pdf

A novel deep learning approach for classification of EEG motor imagery signals

The title of this technical report says almost everything: this is indeed \"a short bibliography on AI and the arts\". It is presented in four sections: General Arguments, Proposals, and Approaches (31 references); Artificial Intelligence in Music (124 references); Artificial Intelligence in Literature and the Performing Arts (13 references), and Artificial Intelligence and Visual Art (57 references). About a quarter of these have short abstracts. Creating a bibliography can be a monumental task, and this bibliography should be viewed as a good and useful start, though it is by no means complete. For comparison, consider the 4,585-entry bibliography Computer Applications in Music by Deta Davis (A-REditions). No direct comparison is intended (or possible), but my point is that many more papers are likely to exist. As a rough check, I looked for several pre-1990 AI and Music articles and books (including my own, of course) in the bibliography. Out of five papers from well-known sources, only one was listed. On the other hand, I discovered a number of papers in this report that were unknown to me, so I am grateful to have a new source of references. In their introduction, the authors acknowledge the need for more references and even offer.a cup of coffee in reward for each new one. I will be sending a number of contributions, so the next time anyone is in Vienna, the coffee is on me. I hope the authors will continue to collect abstracts and publish an updated report in the future.

Auditory Scene Analysis: The Perceptual Organization of Sound by Albert Bregman (review)

Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices through the utilization of brain waves. It is worth noting that the application of BCI is not limited to medical applications, and hence, the research in this field has gained due attention. Moreover, the significant number of related publications over the past two decades further indicates the consistent improvements and breakthroughs that have been made in this particular field. Nonetheless, it is also worth mentioning that with these improvements, new challenges are constantly discovered. This article provides a comprehensive review of the state-of-the-art of a complete BCI system. First, a brief overview of electroencephalogram (EEG)-based BCI systems is given. Secondly, a considerable number of popular BCI applications are reviewed in terms of electrophysiological control signals, feature extraction, classification algorithms, and performance evaluation metrics. Finally, the challenges to the recent BCI systems are discussed, and possible solutions to mitigate the issues are recommended.

/pdf/current-status-challenges-and-possible-solutions-of-eeg-4el4wek5td.pdf

Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review

This study addresses the task of performing robust and reliable time-delay estimation in signals in noisy and reverberating environments. In contrast to the popular signal processing based methods, this paper proposes to transform the input signals using a deep neural network into another pair of sequences which show high cross correlation at the actual time delay. This is achieved with the help of a novel correlation function based objective function for training the network. The proposed approach is also intrinsically interpretable as it does not lose temporal information. Experimental evaluations are performed for estimating mutual time delays for different types of audio signals such as pulse, speech and musical beats. SyncNet out-performs other classical approaches, such as GCC-PHAT, and some other learning based approaches. 

SyncNet: Correlating Objective for Time Delay Estimation in Audio Signals

An ideal audio retrieval system efficiently and robustly recognizes a short query snippet from an extensive database. However, the performance of well-known audio fingerprinting systems falls short at high signal distortion levels. This paper presents an audio retrieval system that generates noise and reverberation robust audio fingerprints using the contrastive learning framework. Using these fingerprints, the method performs a comprehensive search to identify the query audio and precisely estimate its timestamp in the reference audio. Our framework involves training a CNN to maximize the similarity between pairs of embeddings extracted from clean audio and its corresponding distorted and time-shifted version. We employ a channel-wise spectral-temporal attention mechanism to better discriminate the audio by giving more weight to the salient spectral-temporal patches in the signal. Experimental results indicate that our system is efficient in computation and memory usage while being more accurate, particularly at higher distortion levels, than competing state-of-the-art systems and scalable to a larger database.

Vipul Arora

Papers

SyncNet: Correlating Objective for Time Delay Estimation in Audio Signals

Attention-Based Audio Embeddings for Query-by-Example