Single trial BCI classification accuracy improvement for the novel virtual sound movement-based spatial auditory paradigm

2013 ◽  
Author(s):  
Yohann Lelievre ◽  
Yoshikazu Washizawa ◽  
Tomasz M. Rutkowski
Keyword(s):  
Classification Accuracy ◽  
The Novel ◽  
Single Trial ◽  
Accuracy Improvement

scholarly journals Classification Accuracy Improvement for Small-Size Citrus Pests and Diseases Using Bridge Connections in Deep Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4992
Author(s):  
Shuli Xing ◽  
Malrey Lee
Keyword(s):  
Neural Networks ◽  
Classification Accuracy ◽  
Deep Neural Networks ◽  
Citrus Fruit ◽  
Target Object ◽  
Accuracy Improvement ◽  
Pests And Diseases ◽  
The Rich ◽  
New Feature ◽  
Bridge Connections

Due to the rich vitamin content in citrus fruit, citrus is an important crop around the world. However, the yield of these citrus crops is often reduced due to the damage of various pests and diseases. In order to mitigate these problems, several convolutional neural networks were applied to detect them. It is of note that the performance of these selected models degraded as the size of the target object in the image decreased. To adapt to scale changes, a new feature reuse method named bridge connection was developed. With the help of bridge connections, the accuracy of baseline networks was improved at little additional computation cost. The proposed BridgeNet-19 achieved the highest classification accuracy (95.47%), followed by the pre-trained VGG-19 (95.01%) and VGG-19 with bridge connections (94.73%). The use of bridge connections also strengthens the flexibility of sensors for image acquisition. It is unnecessary to pay more attention to adjusting the distance between a camera and pests and diseases.

scholarly journals Identifying Fragmented Reading and Evaluating Its Influence on Cognition Based on Single Trial Electroencephalogram

2021 ◽  
Vol 15 ◽  
Author(s):  
Jingwen Feng ◽  
Bo Hu ◽  
Jingting Sun ◽  
Junpeng Zhang ◽  
Wen Wang ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Classification Accuracy ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Continuous Performance Task ◽  
Single Trial ◽  
K Nearest Neighbor ◽  
Background Condition ◽  
Frequency Bands ◽  
Feature Sets

Background: The use of social media daily could nurture a fragmented reading habit. However, little is known whether fragmented reading (FR) affects cognition and what are the underlying electroencephalogram (EEG) alterations it may lead to.Purpose: This study aimed to identify whether individuals have FR habits based on the single-trial EEG spectral features using machine learning (ML), as well as to find out the potential cognitive impairment induced by FR.Methods: Subjects were recruited through a questionnaire and divided into FR and noFR groups according to the time they spent on FR per day. Moreover, 64-channel EEG was acquired in Continuous Performance Task (CPT) and segmented into 0.5–1.5 s post-stimulus epochs under cue and background conditions. The sample sizes were as follows: FR in cue condition, 692 trials; noFR in cue condition, 688 trials; FR in background condition, 561 trials; noFR in background condition, 585 trials. For these single-trials, the relative power (RP) of six frequency bands [delta (1–3 Hz), theta (4–7 Hz), alpha (8–13 Hz), beta1 (14–20 Hz), beta2 (21–29 Hz), lower gamma (30–40 Hz)] were extracted as features. After feature selection, the most important feature sets were fed into three ML models, namely Support-Vector Machine (SVM), K-Nearest Neighbor (KNN), and Naive Bayes to perform the identification of FR. RP of six frequency bands was also used as feature sets to conduct classification tasks.Results: The classification accuracy reached up to 96.52% in the SVM model under cue conditions. Specifically, among six frequency bands, the most important features were found in alpha and gamma bands. Gamma achieved the highest classification accuracy (86.69% for cue, 86.45% for background). In both conditions, alpha RP in central sites of FR was stronger than noFR (p < 0.001). Gamma RP in the frontal site of FR was weaker than noFR in the background condition (p < 0.001), while alpha RP in parieto-occipital sites of FR was stronger than noFR in the cue condition (p < 0.001).Conclusion: Fragmented reading can be identified based on single-trial EEG evoked by CPT using ML, and the RP of alpha and gamma may reflect the impairment on attention and working memory by FR. FR might lead to cognitive impairment and is worth further exploration.

scholarly journals Simple but Robust Improvement in Multivoxel Pattern Classification

2018 ◽  
Author(s):  
Sangil Lee ◽  
Joseph W. Kable
Keyword(s):  
Classification Accuracy ◽  
Pattern Analysis ◽  
Computational Cost ◽  
Real Data ◽  
Single Trial ◽  
Multivoxel Pattern Analysis ◽  
Direct Benefits ◽  
Mean Centering ◽  
The Mean ◽  
Positive Correlations

AbstractMultivoxel pattern analysis (MVPA) typically begins with the estimation of single trial activation levels, and several studies have examined how different procedures for estimating single trial activity affect the ultimate classification accuracy of MVPA. Here we show that the currently preferred estimation procedures impart spurious shifts in run-level means that cause the estimated activities to be misaligned across runs. These shifts are caused by positive correlations between the means of different category activity estimates within the same scanner run. In other words, if the mean of the estimates for one type of trials is high (low) in a given scanner run, then the mean of the other type of trials is also high (low) for that same scanner run, and the mean across all trials therefore shifts from run to run. Simulations show that these correlations are unavoidable whenever there is a need to deconvolve overlapping trial activities in the presence of noise. We show that subtracting the mean across all trials of a run from all the estimates within that run (i.e., run-level mean centering of estimates), by cancelling out these mean shifts, leads to robust and significant improvements in MVPA classification accuracy. These improvements are seen in both simulated and real data across a wide variety of situations and can provide significant direct benefits with no computational cost. However, we also point out that there could be cases when mean activations are expected to shift across runs and that run-level mean centering could be detrimental in some of these cases (e.g., different proportion of trial types between different runs).

scholarly journals A Never-Ending Learning Method for Fault Diagnostics in Energy Systems Operating in Evolving Environments

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4802 ◽  
Author(s):  
Maria Rosaria Termite ◽  
Piero Baraldi ◽  
Sameer Al-Dahidi ◽  
Luca Bellani ◽  
Michele Compare ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Nearest Neighbor ◽  
Energy Systems ◽  
Energy Industry ◽  
The Novel ◽  
Operational Conditions ◽  
Nearest Neighbor Classifier ◽  
Tank Pressure ◽  
Neighbor Classifier

Condition monitoring (CM) in the energy industry is limited by the lack of pre-classified data about the normal and/or abnormal plant states and the continuous evolution of its operational conditions. The objective is to develop a CM model able to: (1) Detect abnormal conditions and classify the type of anomaly; (2) recognize novel plant behaviors; (3) select representative examples of the novel classes for labeling by an expert; (4) automatically update the CM model. A CM model based on the never-ending learning paradigm is developed. It develops a dictionary containing labeled prototypical subsequences of signal values representing normal conditions and anomalies, which is continuously updated by using a dendrogram to identify groups of similar subsequences of novel classes and to select those subsequences to be labelled by an expert. A 1-nearest neighbor classifier is trained to online detect abnormal conditions and classify their types. The proposed CM model is applied to a synthetic case study and a real case study concerning the monitoring of the tank pressure of an aero derivative gas turbine lube oil system. The CM model provides satisfactory performances in terms of classification accuracy, while remarkably reducing the expert efforts for data labeling and model (periodic) updating.

Classification accuracy improvement by data preprocessing in handheld laser-induced breakdown spectroscopy

2019 ◽  
Vol 11 (40) ◽  
pp. 5177-5184 ◽  
Author(s):  
Jiujiang Yan ◽  
Ping Yang ◽  
Ran Zhou ◽  
Shuhan Li ◽  
Kun Liu ◽  
...  
Keyword(s):  
Qualitative Analysis ◽  
Classification Accuracy ◽  
Data Preprocessing ◽  
Laser Induced Breakdown Spectroscopy ◽  
Accuracy Improvement ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

Qualitative analysis using handheld laser-induced breakdown spectroscopy (HH-LIBS) usually suffers from spectral fluctuation.

A New Single Trial P300 Classification Method

2012 ◽  
Vol 3 (4) ◽  
pp. 31-41 ◽  
Author(s):  
Kun Li ◽  
Ravi Sankar ◽  
Ke Cao ◽  
Yael Arbel ◽  
Emanuel Donchin
Keyword(s):  
Classification Accuracy ◽  
Data Communication ◽  
Processing Technique ◽  
Single Trial ◽  
Linear Discriminant ◽  
P300 Speller ◽  
Trial Analysis ◽  
Simple Processing ◽  
Communication Ability ◽  
Processing Techniques

P300-Speller is one of the most practical and widely used Brain Computer Interface (BCI) for locked-in people who are not able to communicate with others via traditional communication methods. Many signal processing techniques have been utilized in P300-Speller to restore the communication ability of these locked-in people. These techniques are capable of achieving high classification accuracy. However the classification accuracy dramatically decreases for single trial analysis. The reason for that is that the noises existing in the recorded signals are usually removed by averaging several trials. When only a single trial is available, averaging is no longer an option for de-noising. The “averaging” step becomes the bottle neck of P300 response detection which highly limits the processing speed. Researchers are looking for techniques that can accomplish the classification task in a single trial. In this work, a new, effective but simple processing technique for single trial electroencephalography (EEG) classification using variance analysis based method is presented. This method achieved an overall accuracy of 84.8% for single trial P300 response identification. When compared with a single trial stepwise linear discriminant analysis (SWLDA), the authors’ method in terms of overall accuracy is more accurate and the data communication speed is significantly improved.

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