scholarly journals Auditory Attention Detection via Cross-Modal Attention

2021 ◽  
Vol 15 ◽  
Author(s):  
Siqi Cai ◽  
Peiwen Li ◽  
Enze Su ◽  
Longhan Xie
Keyword(s):  
Data Visualization ◽  
Real World ◽  
State Of The Art ◽  
Auditory Attention ◽  
Eeg Signals ◽  
Brain Signals ◽  
Novel Approach ◽  
Perceptual Ability ◽  
Short Signal ◽  
Eeg Features

Humans show a remarkable perceptual ability to select the speech stream of interest among multiple competing speakers. Previous studies demonstrated that auditory attention detection (AAD) can infer which speaker is attended by analyzing a listener's electroencephalography (EEG) activities. However, previous AAD approaches perform poorly on short signal segments, more advanced decoding strategies are needed to realize robust real-time AAD. In this study, we propose a novel approach, i.e., cross-modal attention-based AAD (CMAA), to exploit the discriminative features and the correlation between audio and EEG signals. With this mechanism, we hope to dynamically adapt the interactions and fuse cross-modal information by directly attending to audio and EEG features, thereby detecting the auditory attention activities manifested in brain signals. We also validate the CMAA model through data visualization and comprehensive experiments on a publicly available database. Experiments show that the CMAA achieves accuracy values of 82.8, 86.4, and 87.6% for 1-, 2-, and 5-s decision windows under anechoic conditions, respectively; for a 2-s decision window, it achieves an average of 84.1% under real-world reverberant conditions. The proposed CMAA network not only achieves better performance than the conventional linear model, but also outperforms the state-of-the-art non-linear approaches. These results and data visualization suggest that the CMAA model can dynamically adapt the interactions and fuse cross-modal information by directly attending to audio and EEG features in order to improve the AAD performance.

EFFICIENT AUTOMATIC SELECTION AND COMBINATION OF EEG FEATURES IN LEAST SQUARES CLASSIFIERS FOR MOTOR IMAGERY BRAIN–COMPUTER INTERFACES

2013 ◽  
Vol 23 (04) ◽  
pp. 1350015 ◽  
Author(s):  
GERMAN RODRÍGUEZ-BERMÚDEZ ◽  
PEDRO J. GARCÍA-LAENCINA ◽  
JOAQUÍN ROCA-DORDA
Keyword(s):  
Least Squares ◽  
Motor Imagery ◽  
Time Course ◽  
State Of The Art ◽  
Eeg Signals ◽  
Least Angle Regression ◽  
Wilcoxon Rank Sum Test ◽  
Leave One Out ◽  
Two Stages ◽  
Eeg Features

Discriminative features have to be properly extracted and selected from the electroencephalographic (EEG) signals of each specific subject in order to achieve an adaptive brain–computer interface (BCI) system. This work presents an efficient wrapper-based methodology for feature selection and least squares discrimination of high-dimensional EEG data with low computational complexity. Features are computed in different time segments using three widely used methods for motor imagery tasks and, then, they are concatenated or averaged in order to take into account the time course variability of the EEG signals. Once EEG features have been extracted, proposed framework comprises two stages. The first stage entails feature ranking and, in this work, two different procedures have been considered, the least angle regression (LARS) and the Wilcoxon rank sum test, to compare the performance of each one. The second stage selects the most relevant features using an efficient leave-one-out (LOO) estimation based on the Allen's PRESS statistic. Experimental comparisons with the state-of-the-art BCI methods shows that this approach gives better results than current state-of-the-art approaches in terms of recognition rates and computational requirements and, also with respect to the first ranking stage, it is confirmed that the LARS algorithm provides better results than the Wilcoxon rank sum test for these experiments.

scholarly journals A Novel Approach to Study the Effects of Anesthesia on Respiratory Signals by using the EEG Signals

2017 ◽  
Vol 6 (2) ◽  
pp. 117
Author(s):  
Mohd Suhaib Kidwai ◽  
S. Hasan Saeed
Keyword(s):  
General Anesthesia ◽  
Phase Synchronization ◽  
Eeg Signals ◽  
Drug Induced ◽  
Anesthetic Depth ◽  
Brain Signals ◽  
Novel Approach ◽  
Respiratory Parameters ◽  
Single Coupling ◽  
Separate Subsystem

General anesthesia plays a crucial role in many surgical procedures. It is a drug-induced, reversible state characterized by unconsciousness, anti-nociception or analgesia, immobility and amnesia. On rare occasions, however, the patient can remain unconscious longer than intended, or may regain awareness during surgery. There are no precise measures for maintaining the correct dose of anesthetic, and there is currently no fully reliable instrument to monitor depth of anesthesia. Although a number of devices for monitoring brain function or sympathetic output are commercially available, the anesthetist also relies on clinical assessment and experience to judge anesthetic depth. The undesirable consequences of overdose or unintended awareness might in principle be ameliorated by improved control if we could understand better the changes in function that occur during general anesthesia. Coupling functions prescribe the physical rule specifying how the inter-oscillator interactions occur. They determine the possibility of qualitative transitions between the oscillations, e.g. routes into and out of phase synchronization. Their decomposition can describe the functional contribution from each separate subsystem within a single coupling relationship. In this way, coupling functions offer a unique means of describing mechanisms in a unified and mathematically precise way. It is a fast growing field of research, with much recent progress on the theory and especially towards being able to extract and reconstruct the coupling functions between interacting oscillations from data, leading to useful applications in cardio respiratory interactions.<br />In this paper, a novel approach has been proposed for detecting the changes in synchronism of brain signals, taken from EEG machine. During the effect of anesthesia, there are certain changes in the EEG signals. Those signals show changes in their synchronism. This phenomenon of synchronism can be utilized to study the effect of anesthesia on respiratory parameters like respiration rate etc, and hence the quantity of anesthesia can be regulated, and if any problem occurs in breathing during the effect of anesthesia on patient, that can also be monitored

scholarly journals Emergency Braking Intention Detect System Based on K-Order Propagation Number Algorithm: A Network Perspective

2021 ◽  
Vol 11 (11) ◽  
pp. 1424
Author(s):  
Yuhong Zhang ◽  
Yuan Liao ◽  
Yudi Zhang ◽  
Liya Huang
Keyword(s):  
Classification Accuracy ◽  
Active Regions ◽  
Classification Performance ◽  
Sensor Data ◽  
Single Trial ◽  
Eeg Signals ◽  
Emergency Braking ◽  
Node Importance ◽  
Novel Approach ◽  
Eeg Features

In order to avoid erroneous braking responses when vehicle drivers are faced with a stressful setting, a K-order propagation number algorithm–Feature selection–Classification System (KFCS)is developed in this paper to detect emergency braking intentions in simulated driving scenarios using electroencephalography (EEG) signals. Two approaches are employed in KFCS to extract EEG features and to improve classification performance: the K-Order Propagation Number Algorithm is the former, calculating the node importance from the perspective of brain networks as a novel approach; the latter uses a set of feature extraction algorithms to adjust the thresholds. Working with the data collected from seven subjects, the highest classification accuracy of a single trial can reach over 90%, with an overall accuracy of 83%. Furthermore, this paper attempts to investigate the mechanisms of brain activeness under two scenarios by using a topography technique at the sensor-data level. The results suggest that the active regions at two states is different, which leaves further exploration for future investigations.

scholarly journals Summarizing Source Code with Transferred API Knowledge

2018 ◽  
Author(s):  
Xing Hu ◽  
Ge Li ◽  
Xin Xia ◽  
David Lo ◽  
Shuai Lu ◽  
...  
Keyword(s):  
Real World ◽  
Software Maintenance ◽  
Large Scale ◽  
State Of The Art ◽  
Source Code ◽  
Code Search ◽  
Novel Approach ◽  
Software Maintenance And Evolution ◽  
World Industry ◽  
Similar Code

Code summarization, aiming to generate succinct natural language description of source code, is extremely useful for code search and code comprehension. It has played an important role in software maintenance and evolution. Previous approaches generate summaries by retrieving summaries from similar code snippets. However, these approaches heavily rely on whether similar code snippets can be retrieved, how similar the snippets are, and fail to capture the API knowledge in the source code, which carries vital information about the functionality of the source code. In this paper, we propose a novel approach, named TL-CodeSum, which successfully uses API knowledge learned in a different but related task to code summarization. Experiments on large-scale real-world industry Java projects indicate that our approach is effective and outperforms the state-of-the-art in code summarization.

scholarly journals A Novel Approach to Study the Effects of Anesthesia on Respiratory Signals by using the EEG Signals

2017 ◽  
Vol 7 (6) ◽  
pp. 3313
Author(s):  
Mohd Suhaib Kidwai ◽  
S. Hasan Saeed
Keyword(s):  
General Anesthesia ◽  
Surgical Procedures ◽  
Crucial Role ◽  
Depth Of Anesthesia ◽  
Eeg Signals ◽  
Brain Signals ◽  
Correct Dose ◽  
Novel Approach ◽  
Respiratory Parameters ◽  
Reliable Instrument

General anesthesia plays a crucial role in many surgical procedures, and it therefore has an enormous impact on human health. There are no precise measures for maintaining the correct dose of anesthetic, and there is currently no fully reliable instrument to monitor depth of anesthesia. In this paper, a novel approach has been proposed for detecting the changes in synchronism of brain signals, taken from EEG machine. During the effect of anesthesia, there are certain changes in the EEG signals. Those signals show changes in their synchronism. This phenomenon of synchronism can be utilized to study the effect of anesthesia on respiratory parameters like respiration rate etc, and hence the quantity of anesthesia can be regulated, and if any problem occurs in breathing during the effect of anesthesia on patient, that can also be monitored.

scholarly journals Compressing deep graph convolution network with multi-staged knowledge distillation

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256187
Author(s):  
Junghun Kim ◽  
Jinhong Jung ◽  
U. Kang
Keyword(s):  
Embedded Systems ◽  
Real World ◽  
State Of The Art ◽  
Significant Loss ◽  
Second Best ◽  
Novel Approach ◽  
Feature Aggregation ◽  
Knowledge Distillation ◽  
Real World Datasets ◽  
Effective Layer

Given a trained deep graph convolution network (GCN), how can we effectively compress it into a compact network without significant loss of accuracy? Compressing a trained deep GCN into a compact GCN is of great importance for implementing the model to environments such as mobile or embedded systems, which have limited computing resources. However, previous works for compressing deep GCNs do not consider the multi-hop aggregation of the deep GCNs, though it is the main purpose for their multiple GCN layers. In this work, we propose MustaD (Multi-staged knowledge Distillation), a novel approach for compressing deep GCNs to single-layered GCNs through multi-staged knowledge distillation (KD). MustaD distills the knowledge of 1) the aggregation from multiple GCN layers as well as 2) task prediction while preserving the multi-hop feature aggregation of deep GCNs by a single effective layer. Extensive experiments on four real-world datasets show that MustaD provides the state-of-the-art performance compared to other KD based methods. Specifically, MustaD presents up to 4.21%p improvement of accuracy compared to the second-best KD models.

NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

2019 ◽  
pp. 139-145
Author(s):  
Selma Büyükgöze
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Electrode Placement ◽  
Eeg Signals ◽  
Non Invasive ◽  
Brain Signals ◽  
Brain States ◽  
The Brain

Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves these frequencies are named and in which brain states these waves occur. 10-20 electrode placement plan for EEG to be placed on the scalp will be shown.

scholarly journals Deep Learning for Transient Image Reconstruction from ToF Data

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1962
Author(s):  
Enrico Buratto ◽  
Adriano Simonetto ◽  
Gianluca Agresti ◽  
Henrik Schäfer ◽  
Pietro Zanuttigh
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Light Response ◽  
Real Data ◽  
Depth Image ◽  
Learning Approach ◽  
Multiple Reflections ◽  
Noisy Input ◽  
Novel Approach ◽  
Incoming Light

In this work, we propose a novel approach for correcting multi-path interference (MPI) in Time-of-Flight (ToF) cameras by estimating the direct and global components of the incoming light. MPI is an error source linked to the multiple reflections of light inside a scene; each sensor pixel receives information coming from different light paths which generally leads to an overestimation of the depth. We introduce a novel deep learning approach, which estimates the structure of the time-dependent scene impulse response and from it recovers a depth image with a reduced amount of MPI. The model consists of two main blocks: a predictive model that learns a compact encoded representation of the backscattering vector from the noisy input data and a fixed backscattering model which translates the encoded representation into the high dimensional light response. Experimental results on real data show the effectiveness of the proposed approach, which reaches state-of-the-art performances.

scholarly journals A New Multi-Person Pose Estimation Method Using the Partitioned CenterPose Network

2021 ◽  
Vol 11 (9) ◽  
pp. 4241
Author(s):  
Jiahua Wu ◽  
Hyo Jong Lee
Keyword(s):  
Pose Estimation ◽  
Human Body ◽  
State Of The Art ◽  
Estimation Method ◽  
Bottom Up ◽  
Center Point ◽  
Novel Approach ◽  
Body Joints

In bottom-up multi-person pose estimation, grouping joint candidates into the appropriately structured corresponding instance of a person is challenging. In this paper, a new bottom-up method, the Partitioned CenterPose (PCP) Network, is proposed to better cluster the detected joints. To achieve this goal, we propose a novel approach called Partition Pose Representation (PPR) which integrates the instance of a person and its body joints based on joint offset. PPR leverages information about the center of the human body and the offsets between that center point and the positions of the body’s joints to encode human poses accurately. To enhance the relationships between body joints, we divide the human body into five parts, and then, we generate a sub-PPR for each part. Based on this PPR, the PCP Network can detect people and their body joints simultaneously, then group all body joints according to joint offset. Moreover, an improved l1 loss is designed to more accurately measure joint offset. Using the COCO keypoints and CrowdPose datasets for testing, it was found that the performance of the proposed method is on par with that of existing state-of-the-art bottom-up methods in terms of accuracy and speed.

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