scholarly journals Mental Task Evaluation for Hybrid NIRS-EEG Brain-Computer Interfaces

2017 ◽  
Vol 2017 ◽  
pp. 1-24 ◽  
Author(s):  
Hubert Banville ◽  
Rishabh Gupta ◽  
Tiago H. Falk
Keyword(s):  
Near Infrared ◽  
Mental Arithmetic ◽  
Binary Classification ◽  
Time Windows ◽  
Brain Regions ◽  
Classification Performance ◽  
Response Patterns ◽  
Word Generation ◽  
Nirs Data ◽  
Accuracy Increase

Based on recent electroencephalography (EEG) and near-infrared spectroscopy (NIRS) studies that showed that tasks such as motor imagery and mental arithmetic induce specific neural response patterns, we propose a hybrid brain-computer interface (hBCI) paradigm in which EEG and NIRS data are fused to improve binary classification performance. We recorded simultaneous NIRS-EEG data from nine participants performing seven mental tasks (word generation, mental rotation, subtraction, singing and navigation, and motor and face imagery). Classifiers were trained for each possible pair of tasks using (1) EEG features alone, (2) NIRS features alone, and (3) EEG and NIRS features combined, to identify the best task pairs and assess the usefulness of a multimodal approach. The NIRS-EEG approach led to an average increase in peak kappa of 0.03 when using features extracted from one-second windows (equivalent to an increase of 1.5% in classification accuracy for balanced classes). The increase was much stronger (0.20, corresponding to an 10% accuracy increase) when focusing on time windows of high NIRS performance. The EEG and NIRS analyses further unveiled relevant brain regions and important feature types. This work provides a basis for future NIRS-EEG hBCI studies aiming to improve classification performance toward more efficient and flexible BCIs.

scholarly journals Crossing time windows optimization based on mutual information for hybrid BCI

2021 ◽  
Vol 18 (6) ◽  
pp. 7919-7935
Author(s):  
Ming Meng ◽  
◽  
Luyang Dai ◽  
Qingshan She ◽  
Yuliang Ma ◽  
...  
Keyword(s):  
Mutual Information ◽  
Mental Arithmetic ◽  
Time Windows ◽  
Classification Performance ◽  
Common Spatial Pattern ◽  
Linear Discriminant ◽  
Information Time ◽  
Crossing Time ◽  
The Moment ◽  
Optimal Window

<abstract> <p>Hybrid EEG-fNIRS brain-computer interface (HBCI) is widely employed to enhance BCI performance. EEG and fNIRS signals are combined to increase the dimensionality of the information. Time windows are used to select EEG and fNIRS singles synchronously. However, it ignores that specific modal signals have their own characteristics, when the task is stimulated, the information between the modalities will mismatch at the moment, which has a significant impact on the classification performance. Here we propose a novel crossing time windows optimization for mental arithmetic (MA) based BCI. The EEG and fNIRS signals were segmented separately by sliding time windows. Then crossing time windows (CTW) were combined with each one segment from EEG and fNIRS selected independently. Furthermore, EEG and fNIRS features were extracted using Filter Bank Common Spatial Pattern (FBCSP) and statistical methods from each sample. Mutual information was calculated for FBCSP and statistical features to characterize the discrimination of crossing time windows, and the optimal window would be selected based on the largest mutual information. Finally, a sparse structured framework of Fisher Lasso feature selection (FLFS) was designed to select the joint features, and conventional Linear Discriminant Analysis (LDA) was employed to perform classification. We used proposed method for a MA dataset. The classification accuracy of the proposed method is 92.52 ± 5.38% and higher than other methods, which shows the rationality and superiority of the proposed method.</p> </abstract>

scholarly journals Data-driven identification of diagnostically useful extrastriatal signal in dopamine transporter SPECT using explainable AI

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mahmood Nazari ◽  
Andreas Kluge ◽  
Ivayla Apostolova ◽  
Susanne Klutmann ◽  
Sharok Kimiaei ◽  
...  
Keyword(s):  
Dopamine Transporter ◽  
Binary Classification ◽  
Temporal Cortex ◽  
Brain Regions ◽  
Classification Performance ◽  
Data Driven ◽  
3 Dimensional ◽  
Parkinsonian Syndromes ◽  
Custom Made ◽  
Spect Images

AbstractThis study used explainable artificial intelligence for data-driven identification of extrastriatal brain regions that can contribute to the interpretation of dopamine transporter SPECT with 123I-FP-CIT in parkinsonian syndromes. A total of 1306 123I-FP-CIT-SPECT were included retrospectively. Binary classification as ‘reduced’ or ‘normal’ striatal 123I-FP-CIT uptake by an experienced reader served as standard-of-truth. A custom-made 3-dimensional convolutional neural network (CNN) was trained for classification of the SPECT images with 1006 randomly selected images in three different settings: “full image”, “striatum only” (3-dimensional region covering the striata cropped from the full image), “without striatum” (full image with striatal region removed). The remaining 300 SPECT images were used to test the CNN classification performance. Layer-wise relevance propagation (LRP) was used for voxelwise quantification of the relevance for the CNN-based classification in this test set. Overall accuracy of CNN-based classification was 97.0%, 95.7%, and 69.3% in the “full image”, “striatum only”, and “without striatum” setting. Prominent contributions in the LRP-based relevance maps beyond the striatal signal were detected in insula, amygdala, ventromedial prefrontal cortex, thalamus, anterior temporal cortex, superior frontal lobe, and pons, suggesting that 123I-FP-CIT uptake in these brain regions provides clinically useful information for the differentiation of neurodegenerative and non-neurodegenerative parkinsonian syndromes.

scholarly journals Comparison of Activation in the Prefrontal Cortex of Native Speakers of Mandarin by Ability of Japanese as a Second Language Using a Novel Speaking Task

Healthcare ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 412
Author(s):  
Li Cong ◽  
Hideki Miyaguchi ◽  
Chinami Ishizuki
Keyword(s):  
Second Language ◽  
Prefrontal Cortex ◽  
Cognitive Load ◽  
Near Infrared ◽  
Native Speakers ◽  
Brain Activation ◽  
Brain Regions ◽  
Strong Inhibition ◽  
Functional Near Infrared Spectroscopy ◽  
High Level

Evidence shows that second language (L2) learning affects cognitive function. Here in this work, we compared brain activation in native speakers of Mandarin (L1) who speak Japanese (L2) between and within two groups (high and low L2 ability) to determine the effect of L2 ability in L1 and L2 speaking tasks, and to map brain regions involved in both tasks. The brain activation during task performance was determined using prefrontal cortex blood flow as a proxy, measured by functional near-infrared spectroscopy (fNIRS). People with low L2 ability showed much more brain activation when speaking L2 than when speaking L1. People with high L2 ability showed high-level brain activation when speaking either L2 or L1. Almost the same high-level brain activation was observed in both ability groups when speaking L2. The high level of activation in people with high L2 ability when speaking either L2 or L1 suggested strong inhibition of the non-spoken language. A wider area of brain activation in people with low compared with high L2 ability when speaking L2 is considered to be attributed to the cognitive load involved in code-switching L1 to L2 with strong inhibition of L1 and the cognitive load involved in using L2.

scholarly journals A Multichannel fNIRS System for Prefrontal Mental Task Classification with Dual-level Excitation and Deep Forest Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Cheng Chen ◽  
Yizhen Wen ◽  
Shaoyang Cui ◽  
Xiangao Qi ◽  
Zhenhong Liu ◽  
...  
Keyword(s):  
Near Infrared ◽  
Continuous Wave ◽  
Mental Arithmetic ◽  
Digit Span ◽  
Structural Features ◽  
Cortical Activation ◽  
Semantic Task ◽  
High Classification Accuracy ◽  
Task Classification ◽  
Deep Forest

This paper presents a multichannel functional continuous-wave near-infrared spectroscopy (fNIRS) system, which collects data under a dual-level light intensity mode to optimize SNR for channels with multiple source-detector separations. This system is applied to classify different cortical activation states of the prefrontal cortex (PFC). Mental arithmetic, digit span, semantic task, and rest state were selected as four mental tasks. A deep forest algorithm is employed to achieve high classification accuracy. By employing multigrained scanning to fNIRS data, this system can extract the structural features and result in higher performance. The proposed system with proper optimization can achieve 86.9% accuracy on the self-built dataset, which is the highest result compared to the existing systems.

scholarly journals Protected Geographical Indication Identification of a Chinese Green Tea (Anji-White) by Near-Infrared Spectroscopy and Chemometric Class Modeling Techniques

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Lu Xu ◽  
Peng-Tao Shi ◽  
Xian-Shu Fu ◽  
Hai-Feng Cui ◽  
Zi-Hong Ye ◽  
...  
Keyword(s):  
Green Tea ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Rapid Identification ◽  
Classification Performance ◽  
Chinese Market ◽  
White Tea ◽  
Modeling Techniques ◽  
Tea Plantations ◽  
Model Training

This paper reports a rapid identification method for a Chinese green tea with PGI, Anji-white tea, by class modeling techniques and NIR spectroscopy. 167 real and representative Anji-white tea samples were collected from 8 tea plantations in their original producing areas for model training. Another 81 non-Anji-white tea samples of similar appearance were collected from 7 important tea producing areas and used for validation of model specificity. Diffuse NIR spectra were measured with finely ground tea powders. OCPLS and SIMCA were used to describe the distribution of representative Anji-white tea objects and predict the authenticity of new objects. For data preprocessing, smoothing, derivatives, and SNV were applied to improve the raw spectra and classification performance. It is demonstrated that taking derivatives and SNV can improve classification accuracy and reduce the complexity of class models by removing spectral background and baseline. For the best models, the sensitivity and specificity were 0.886 and 0.951 for OCPLS, 0.886 and 0.938 for SIMCA with SNV spectra, respectively. Although it is difficult to perform an exhaustive analysis of all types of potential false objects, the proposed method can detect most of the important non-Anji-white teas in the Chinese market.

scholarly journals Depression Disorder Classification of fMRI Data Using Sparse Low-Rank Functional Brain Network and Graph-Based Features

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Xin Wang ◽  
Yanshuang Ren ◽  
Wensheng Zhang
Keyword(s):  
Pearson Correlation ◽  
Brain Regions ◽  
Brain Network ◽  
Classification Performance ◽  
Low Rank ◽  
Fmri Data ◽  
Healthy Controls ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Depression Disorder

Study of functional brain network (FBN) based on functional magnetic resonance imaging (fMRI) has proved successful in depression disorder classification. One popular approach to construct FBN is Pearson correlation. However, it only captures pairwise relationship between brain regions, while it ignores the influence of other brain regions. Another common issue existing in many depression disorder classification methods is applying only single local feature extracted from constructed FBN. To address these issues, we develop a new method to classify fMRI data of patients with depression and healthy controls. First, we construct the FBN using a sparse low-rank model, which considers the relationship between two brain regions given all the other brain regions. Moreover, it can automatically remove weak relationship and retain the modular structure of FBN. Secondly, FBN are effectively measured by eight graph-based features from different aspects. Tested on fMRI data of 31 patients with depression and 29 healthy controls, our method achieves 95% accuracy, 96.77% sensitivity, and 93.10% specificity, which outperforms the Pearson correlation FBN and sparse FBN. In addition, the combination of graph-based features in our method further improves classification performance. Moreover, we explore the discriminative brain regions that contribute to depression disorder classification, which can help understand the pathogenesis of depression disorder.

scholarly journals Analysis of Different Classification Techniques for Two-Class Functional Near-Infrared Spectroscopy-Based Brain-Computer Interface

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Noman Naseer ◽  
Nauman Khalid Qureshi ◽  
Farzan Majeed Noori ◽  
Keum-Shik Hong
Keyword(s):  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Continuous Wave ◽  
Mental Arithmetic ◽  
Statistical Significance ◽  
Support Vector ◽  
Functional Near Infrared Spectroscopy ◽  
Linear Discriminant

We analyse and compare the classification accuracies of six different classifiers for a two-class mental task (mental arithmetic and rest) using functional near-infrared spectroscopy (fNIRS) signals. The signals of the mental arithmetic and rest tasks from the prefrontal cortex region of the brain for seven healthy subjects were acquired using a multichannel continuous-wave imaging system. After removal of the physiological noises, six features were extracted from the oxygenated hemoglobin (HbO) signals. Two- and three-dimensional combinations of those features were used for classification of mental tasks. In the classification, six different modalities, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA),k-nearest neighbour (kNN), the Naïve Bayes approach, support vector machine (SVM), and artificial neural networks (ANN), were utilized. With these classifiers, the average classification accuracies among the seven subjects for the 2- and 3-dimensional combinations of features were 71.6, 90.0, 69.7, 89.8, 89.5, and 91.4% and 79.6, 95.2, 64.5, 94.8, 95.2, and 96.3%, respectively. ANN showed the maximum classification accuracies: 91.4 and 96.3%. In order to validate the results, a statistical significance test was performed, which confirmed that thepvalues were statistically significant relative to all of the other classifiers (p< 0.005) using HbO signals.

scholarly journals Cerebrovascular responses of the rat brain to noxious stimuli as examined by functional near-infrared whole brain imaging

2012 ◽  
Vol 107 (10) ◽  
pp. 2853-2865 ◽  
Author(s):  
Ji-Wei He ◽  
Fenghua Tian ◽  
Hanli Liu ◽  
Yuan Bo Peng
Keyword(s):  
Brain Imaging ◽  
Near Infrared ◽  
Small Animal ◽  
Nir Spectroscopy ◽  
Brain Regions ◽  
Anterior Cingulate ◽  
Hemodynamic Changes ◽  
Whole Brain ◽  
Brain Hemodynamics

While near-infrared (NIR) spectroscopy has been increasingly used to detect stimulated brain activities with an advantage of dissociating regional oxy- and deoxyhemoglobin concentrations simultaneously, it has not been utilized much in pain research. Here, we investigated and demonstrated the feasibility of using this technique to obtain whole brain hemodynamics in rats and speculated on the functional relevance of the NIR-based hemodynamic signals during pain processing. NIR signals were emitted and collected using a 26-optodes array on rat's dorsal skull surface after the removal of skin. Following the subcutaneous injection of formalin (50 μl, 3%) into a hindpaw, several isolable brain regions showed hemodynamic changes, including the anterior cingulate cortex, primary/secondary somatosensory cortexes, thalamus, and periaqueductal gray ( n = 6). Time courses of hemodynamic changes in respective regions matched with the well-documented biphasic excitatory response. Surprisingly, an atypical pattern (i.e., a decrease in oxyhemoglobin concentration with a concomitant increase in deoxyhemoglobin concentration) was seen in phase II. In a separate group of rats with innocuous brush and noxious pinch of the same area ( n = 11), results confirmed that the atypical pattern occurred more likely in the presence of nociception than nonpainful stimulation, suggesting it as a physiological substrate when the brain processes pain. In conclusion, the NIR whole brain imaging provides a useful alternative to study pain in vivo using small-animal models. Our results support the notion that neurovascular response patterns depend on stimuli, bringing attention to the interpretation of vascular-based neuroimaging data in studies of pain.

scholarly journals Wavelet Autoregulation Monitoring Identifies Blood Pressures Associated With Brain Injury in Neonatal Hypoxic-Ischemic Encephalopathy

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiuyun Liu ◽  
Aylin Tekes ◽  
Jamie Perin ◽  
May W. Chen ◽  
Bruno P. Soares ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Brain Injury ◽  
Therapeutic Hypothermia ◽  
Near Infrared ◽  
Brain Regions ◽  
Hypoxic Ischemic Encephalopathy ◽  
Volume Index ◽  
Arterial Blood ◽  
Blood Pressures ◽  
Ischemic Encephalopathy

Dysfunctional cerebrovascular autoregulation may contribute to neurologic injury in neonatal hypoxic-ischemic encephalopathy (HIE). Identifying the optimal mean arterial blood pressure (MAPopt) that best supports autoregulation could help identify hemodynamic goals that support neurologic recovery. In neonates who received therapeutic hypothermia for HIE, we hypothesized that the wavelet hemoglobin volume index (wHVx) would identify MAPopt and that blood pressures closer to MAPopt would be associated with less brain injury on MRI. We also tested a correlation-derived hemoglobin volume index (HVx) and single- and multi-window data processing methodology. Autoregulation was monitored in consecutive 3-h periods using near infrared spectroscopy in an observational study. The neonates had a mean MAP of 54 mmHg (standard deviation: 9) during hypothermia. Greater blood pressure above the MAPopt from single-window wHVx was associated with less injury in the paracentral gyri (p = 0.044; n = 63), basal ganglia (p = 0.015), thalamus (p = 0.013), and brainstem (p = 0.041) after adjustments for sex, vasopressor use, seizures, arterial carbon dioxide level, and a perinatal insult score. Blood pressure exceeding MAPopt from the multi-window, correlation HVx was associated with less injury in the brainstem (p = 0.021) but not in other brain regions. We conclude that applying wavelet methodology to short autoregulation monitoring periods may improve the identification of MAPopt values that are associated with brain injury. Having blood pressure above MAPopt with an upper MAP of ~50–60 mmHg may reduce the risk of brain injury during therapeutic hypothermia. Though a cause-and-effect relationship cannot be inferred, the data support the need for randomized studies of autoregulation and brain injury in neonates with HIE.

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