Decoding human mental states by whole-head EEG+fNIRS during category fluency task performance

Omurtag, Ahmet; Aghajani, Haleh; Keleş, Hasan Onur

doi:10.1088/1741-2552/aa814b

Cited by 22 publications

(19 citation statements)

References 77 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In such applications, of paramount importance. Recently multi-modal techniques utilizing concurrent EEG and fNIRS have gained popularity due to their relatively richer information content (Hirshfield et al, 2009 ; Liu Y. et al, 2013 ; Liu T. et al, 2013 ; Keles et al, 2014b ; Aghajani and Omurtag, 2016 ; Buccino et al, 2016 ; Omurtag et al, 2017 ). Available evidence indicates that brain activity measures of MWL are more informative than ocular or peripheral physiology measures (Hogervorst et al, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

Measuring Mental Workload with EEG+fNIRS

Aghajani

Garbey

Omurtag

2017

Front. Hum. Neurosci.

Self Cite

167

111

View full text Add to dashboard Cite

We studied the capability of a Hybrid functional neuroimaging technique to quantify human mental workload (MWL). We have used electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) as imaging modalities with 17 healthy subjects performing the letter n-back task, a standard experimental paradigm related to working memory (WM). The level of MWL was parametrically changed by variation of n from 0 to 3. Nineteen EEG channels were covering the whole-head and 19 fNIRS channels were located on the forehead to cover the most dominant brain region involved in WM. Grand block averaging of recorded signals revealed specific behaviors of oxygenated-hemoglobin level during changes in the level of MWL. A machine learning approach has been utilized for detection of the level of MWL. We extracted different features from EEG, fNIRS, and EEG+fNIRS signals as the biomarkers of MWL and fed them to a linear support vector machine (SVM) as train and test sets. These features were selected based on their sensitivity to the changes in the level of MWL according to the literature. We introduced a new category of features within fNIRS and EEG+fNIRS systems. In addition, the performance level of each feature category was systematically assessed. We also assessed the effect of number of features and window size in classification performance. SVM classifier used in order to discriminate between different combinations of cognitive states from binary- and multi-class states. In addition to the cross-validated performance level of the classifier other metrics such as sensitivity, specificity, and predictive values were calculated for a comprehensive assessment of the classification system. The Hybrid (EEG+fNIRS) system had an accuracy that was significantly higher than that of either EEG or fNIRS. Our results suggest that EEG+fNIRS features combined with a classifier are capable of robustly discriminating among various levels of MWL. Results suggest that EEG+fNIRS should be preferred to only EEG or fNIRS, in developing passive BCIs and other applications which need to monitor users' MWL.

show abstract

Section: Introductionmentioning

confidence: 99%

Measuring Mental Workload with EEG+fNIRS

Aghajani

Garbey

Omurtag

2017

Front. Hum. Neurosci.

Self Cite

167

111

View full text Add to dashboard Cite

show abstract

“…The increasing popularity of fNIRS is owed to its portability, its moderate spatial and temporal resolution, its ease of use, and its ability to scan slowly moving human subjects. All these advantages have encouraged researchers to utilize it individually or along with other modalities such as fMRI [8], [56]- [58] or EEG [59]- [64].…”

Section: Chronology Of Fnirs Evolutionmentioning

confidence: 99%

A Newcomer's Guide to Functional Near Infrared Spectroscopy Experiments

Almajidy

Mankodiya

Abtahi

et al. 2020

IEEE Rev. Biomed. Eng.

View full text Add to dashboard Cite

show abstract

“…Mayer waves) the fNIRS signals were first band-pass filtered in the frequency band 0.5-2 Hz. The signals were converted to hemoglobin concentration changes using the modified Beer-Lambert Law 78,79 .…”

Section: Data Collectionmentioning

confidence: 99%

Physiological correlates of cognitive load in laparoscopic surgery

Zakeri

Mansfield

Sunderland

et al. 2020

Sci Rep

Self Cite

View full text Add to dashboard Cite

Laparoscopic surgery can be exhausting and frustrating, and the cognitive load experienced by surgeons may have a major impact on patient safety as well as healthcare economics. As cognitive load decreases with increasing proficiency, its robust assessment through physiological data can help to develop more effective training and certification procedures in this area. We measured data from 31 novices during laparoscopic exercises to extract features based on cardiac and ocular variables. these were compared with traditional behavioural and subjective measures in a dual-task setting. We found significant correlations between the features and the traditional measures. The subjective task difficulty, reaction time, and completion time were well predicted by the physiology features. Reaction times to randomly timed auditory stimuli were correlated with the mean of the heart rate (r = −0.29) and heart rate variability (r = 0.4). completion times were correlated with the physiologically predicted values with a correlation coefficient of 0.84. We found that the multi-modal set of physiology features was a better predictor than any individual feature and artificial neural networks performed better than linear regression. The physiological correlates studied in this paper, translated into technological products, could help develop standardised and more easily regulated frameworks for training and certification. Laparoscopic surgery (LS) offers substantial clinical and economic benefits over open surgery, including decreased postoperative pain and better utilisation of hospital beds and antibiotics 1-3. Consequently, LS is becoming increasingly routine in many surgical conditions. In the United States in 2013, surgeons performed cholecystectomy laparoscopically in 96% of cases 4. The share of laparoscopy procedures within the total number of appendectomy and cholecystectomies in the United Kingdom in 2017 were respectively 74% and 92%, up from 41 and 84% in 2010 5. During LS, surgeons coordinate their hands, eyes, and long-shaft instruments in trocars inserted at narrow incisions, while mentally translating two-dimensional real-time video into the three-dimensional intracorporal setting. They continuously translate hand movements into the inverted movements of tool-tips and receive limited haptic feedback. The additional difficulties of performing LS relative to open surgery are widely acknowledged 6. These highlight further unmet needs in the context of training and assessment, which would be greatly helped by tools that can provide new insights into skill development 7,8. In addition to technical skills based on visual and motor ability, surgeons generally use an array of non-technical skills that include mental readiness, cognitive flexibility, the ability to anticipate problems, team adaptation, safety awareness, situational awareness and communication style 9. Intraoperative decisions are required when, for example, a surgeon faces an anatomic anomaly or patient decompensation. Rapidly making correct decisions and man...

show abstract

Decoding human mental states by whole-head EEG+fNIRS during category fluency task performance

Abstract: Availability of an accurate and practical decoding method has potential implications for medical diagnosis, brain-computer interface design, and neuroergonomics.

Cited by 22 publications

References 77 publications

Measuring Mental Workload with EEG+fNIRS

Measuring Mental Workload with EEG+fNIRS

A Newcomer's Guide to Functional Near Infrared Spectroscopy Experiments

Physiological correlates of cognitive load in laparoscopic surgery

Contact Info

Product

Resources

About