Determining Optimal Feature-Combination for LDA Classification of Functional Near-Infrared Spectroscopy Signals in Brain-Computer Interface Application

Naseer, Noman; Noori, Farzan Majeed; Qureshi, Nauman Khalid; Hong, Keum–Shik

doi:10.3389/fnhum.2016.00237

Cited by 121 publications

(82 citation statements)

References 75 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Indeed, if the PFC activity were to facilitate the teaching–learning process, its feedback to the teacher during the teaching–learning task might act similarly as a brain–computer interface (BCI) neuro-feedback training. Recently, several studies had reported NIRS-based BCI focusing on the PFC activity (Naseer et al, 2016a,b). In order to evaluate whether the PFC signals during teaching–learning tasks are suitable for NIRS-based BCI training, it is a prerequisite to calculate the classification accuracies of pattern recognition and discrimination in BCI by cross-validation in future studies (Naseer et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

“…The NIRS system used in this study consisted of six emitters and six detectors, resulting in sixteen channels, each consisting of one emitter-detector pair. In general, an emitter-detector distance around 2.5–3.5 cm is applied, because a distance below 2 cm might result in only superficial layer signal capture, while a distance above 4 cm might result in a weak signal (Gratton et al, 2006; Naseer et al, 2016a). In our study, this distance was set to 3.0 cm.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Integration of Teaching Processes and Learning Assessment in the Prefrontal Cortex during a Video Game Teaching–learning Task

et al. 2017

View full text Add to dashboard Cite

Human teaching is a social interaction that supports reciprocal and dynamical feedback between the teacher and the student. The prefrontal cortex (PFC) is a region of particular interest due to its demonstrated role in social interaction. In the present study, we evaluated the PFC activity simultaneously in two individuals playing the role of a teacher and student in a video game teaching–learning task. For that, we used two wearable near-infrared spectroscopy (NIRS) devices in order to elucidate the neural mechanisms underlying cognitive interactions between teachers and students. Fifteen teacher–student pairs in total (N = 30) participated in this study. Each teacher was instructed to teach the video game to their student partner, without speaking. The PFC activity was simultaneously evaluated in both participants using a wearable 16-channel NIRS system during the video game teaching–learning task. Two sessions, each including a triplet of a 30-s teaching–learning task, were performed in order to evaluate changes in PFC activity after advancement of teaching–learning state. Changes in the teachers’ left PFC activity between the first and second session positively correlated with those observed in students (r = 0.694, p = 0.004). Moreover, among teachers, multiple regression analysis revealed a correlation between the left PFC activity and the assessment gap between one’s own teaching and the student’s understanding (β = 0.649, p = 0.009). Activity in the left PFC changed synchronously in both teachers and students after advancement of the teaching–learning state. The left PFC of teachers may be involved in integrating information regarding one’s own teaching process and the student’s learning state. The present observations indicate that simultaneous recording and analysis of brain activity data during teacher–student interactions may be useful in the field of educational neuroscience.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Integration of Teaching Processes and Learning Assessment in the Prefrontal Cortex during a Video Game Teaching–learning Task

et al. 2017

View full text Add to dashboard Cite

show abstract

“…This approach was used in order to test fNIRS based MDB of affective state with the least assumptions possible, to avoid introducing spurious artifact in the signal and to test the feasibility for posterior real-time analysis (Coyle et al, 2007, Sitaram et al, 2007. In comparison, recent studies identified that combining the mean hemoglobin concentration with other temporal and time-frequency features improves the decoding accuracies reaching values close to 90% in within-subject decoding (Tai and Chau, 2009, Naseer et al, 2016a, Naseer et al, 2016b. Therefore, future studies should also evaluate the effect of different feature extraction techniques to the inter-participants MBD of affective states.…”

Section: Limitations and Future Perspectivesmentioning

confidence: 99%

Subject-independent decoding of affective states using functional near-infrared spectroscopy

Trambaiolli

Tossato

Cravo

et al. 2017

Preprint

View full text Add to dashboard Cite

Multivariate brain decoding (MBD) can be applied to estimate mental states using brain signal measurements. In the best scenario, a MBD model should be trained in a first set of volunteers and then validated in a new and independent dataset. Here, we aimed to evaluate whether functional near-infrared spectroscopy (fNIRS) signals from frontal and occipital areas provide enough information to discriminate affective states. For this purpose, a linear discriminant analysis classifier was trained in a first database (49 participants, 24.65±3.23 years) and tested in an independent database (20 participants, 24.00±3.92 years). Significant accuracies were found for positive vs. negative (64.50±12.44%, p<0.01) and negative vs. neutral (67.75±14.45%, p<0.01) affect during a passive elicitation condition, consisting in viewing pre-validated images with emotional content. For the active elicitation condition, in which volunteers were instructed to recollect personal affective experiences, significant accuracy was found for positive vs. neutral affect (71.00±17.93%, p<0.01). In this last case, only three fNIRS channels were sufficient to discriminate between those affective states: two positioned over the left ventrolateral prefrontal area and one over the right lateral orbitofrontal cortex. In conclusion, our results show that fNIRS is a feasible technique for intersubject affective decoding, reaching significant classification accuracies using a few and biologically consistent features.

show abstract

“…In particular, the useful information from the continuous measurement of cortical activities can be extracted and selected to indicate the variations of the human cognitive sate. In recent study, Naseer et al (2016) built a novel brain-computer interface system, where the linear discrimination analysis model is used to classify the functional near-infrared spectroscopy (fNIR) features. Based on the optimal feature combination, the optimal recognition rate of two mental states is achieved.…”

Section: Introductionmentioning

confidence: 99%

Cross-Subject EEG Feature Selection for Emotion Recognition Using Transfer Recursive Feature Elimination

et al. 2017

View full text Add to dashboard Cite

Using machine-learning methodologies to analyze EEG signals becomes increasingly attractive for recognizing human emotions because of the objectivity of physiological data and the capability of the learning principles on modeling emotion classifiers from heterogeneous features. However, the conventional subject-specific classifiers may induce additional burdens to each subject for preparing multiple-session EEG data as training sets. To this end, we developed a new EEG feature selection approach, transfer recursive feature elimination (T-RFE), to determine a set of the most robust EEG indicators with stable geometrical distribution across a group of training subjects and a specific testing subject. A validating set is introduced to independently determine the optimal hyper-parameter and the feature ranking of the T-RFE model aiming at controlling the overfitting. The effectiveness of the T-RFE algorithm for such cross-subject emotion classification paradigm has been validated by DEAP database. With a linear least square support vector machine classifier implemented, the performance of the T-RFE is compared against several conventional feature selection schemes and the statistical significant improvement has been found. The classification rate and F-score achieve 0.7867, 0.7526, 0.7875, and 0.8077 for arousal and valence dimensions, respectively, and outperform several recent reported works on the same database. In the end, the T-RFE based classifier is compared against two subject-generic classifiers in the literature. The investigation of the computational time for all classifiers indicates the accuracy improvement of the T-RFE is at the cost of the longer training time.

show abstract

Determining Optimal Feature-Combination for LDA Classification of Functional Near-Infrared Spectroscopy Signals in Brain-Computer Interface Application

Cited by 121 publications

References 75 publications

Integration of Teaching Processes and Learning Assessment in the Prefrontal Cortex during a Video Game Teaching–learning Task

Integration of Teaching Processes and Learning Assessment in the Prefrontal Cortex during a Video Game Teaching–learning Task

Subject-independent decoding of affective states using functional near-infrared spectroscopy

Cross-Subject EEG Feature Selection for Emotion Recognition Using Transfer Recursive Feature Elimination

Contact Info

Product

Resources

About