fNIRS-based classification of mind-wandering with personalized window selection for multimodal learning interfaces

Liu, Ruixue; Walker, Erin; Friedman, Leah; Arrington, Catherine M.; Solovey, Erin Treacy

doi:10.1007/s12193-020-00325-z

Cited by 12 publications

(8 citation statements)

References 63 publications

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“…However, this has to be compromised with potential fatigue, which was reported by the participants as minimal because the task execution in this experiment lasted for about 25 min on average (std = 2.32 min). Besides, longer trials would enable the application of the sliding window technique, which was found to improve accuracy in detecting a mental state (mind-wandering) (Liu et al, 2020). Repeating the experiment with similar protocol to this study can also allow the comparison of classification methods.…”

Section: Recommendations For Future Workmentioning

confidence: 91%

Exploring the Brain Activity Related to Missing Penalty Kicks: An fNIRS Study

Slutter

Thammasan

Poel

2021

Front. Comput. Sci.

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At vital moments in professional soccer matches, penalties were often missed. Psychological factors, such as anxiety and pressure, are among the critical causes of the mistakes, commonly known as choking under pressure. Nevertheless, the factors have not been fully explored. In this study, we used functional near-infrared spectroscopy (fNIRS) to investigate the influence of the brain on this process. An in-situ study was set-up (N = 22), in which each participant took 15 penalties under three different pressure conditions: without a goalkeeper, with an amiable goalkeeper, and with a competitive goalkeeper. Both experienced and inexperienced soccer players were recruited, and the brain activation was compared across groups. Besides, fNIRS activation was compared between sessions that participants felt anxious against sessions without anxiety report, and between penalty-scoring and -missing sessions. The results show that the task-relevant brain region, the motor cortex, was more activated when players were not experiencing performance anxiety. The activation of task-irrelevant areas was shown to be related to players experiencing anxiety and missing penalties, especially the prefrontal cortex (PFC). More particularly, an overall higher activation of the PFC and an increase of PFC lateral asymmetry were related to anxious players and missed penalties, which can be caused by players' worries about the consequences of scoring or missing the penalty kicks. When experienced players were feeling anxious, their left temporal cortex activation increased, which could be an indication that experienced overthink the situation and neglect their automated skills. Besides, the left temporal cortex activation is higher when inexperienced players succeeded to score a penalty. Overall, the results of this study are in line with the neural efficiency theory and demonstrate the feasibility and ecological validity to detect neurological clues relevant to anxiety and performance from fNIRS recordings in the field.

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Section: Recommendations For Future Workmentioning

confidence: 91%

Exploring the Brain Activity Related to Missing Penalty Kicks: An fNIRS Study

Slutter

Thammasan

Poel

2021

Front. Comput. Sci.

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Section: Fnirs and Mind Wanderingmentioning

confidence: 99%

“…There are relatively few research efforts focused on assessing mind wandering using fNIRS signals. One study investigated different ML and deep learning classifiers such as deep neural networks (DNNs), convolution neural networks (CNNs), and XGBoost to identify different mind wandering levels without interrupting the incoming task [52]. Another study analyzed the participants’ mind wandering levels by exploring when the participants leave the primary task in the Sustained Attention to Response Task (SART) [53].…”

Section: Related Workmentioning

confidence: 99%

Assessing the Effects of Various Physiological Signal Modalities on Predicting Different Human Cognitive States

Aygun,

Nguyen,

Scheutz

2024

Preprint

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Robust estimation of systemic human cognitive states is critical for a variety of applications, from simply detecting inefficiencies in task assignments, to the adaptation of artificial agents’ behaviors to improve team performance in mixed-initiative human-machine teams. This study showed that human eye gaze, in particular, the percentage change in pupil size (PCPS), is the most reliable biomarker for assessing three human cognitive states including workload, sense of urgency, and mind wandering compared to electroencephalogram (EEG), functional near-infrared spectroscopy (fNIRS), respiration, and skin conductance. We used comprehensive multi-modal driving dataset to examine the accuracy of signals to assess these cognitive states. We performed comprehensive statistical tests to validate the performance of several physiological signals to determine human cognitive states and demonstrated that PCPS shows noticeably superior performance. We also characterized the link between workload and sense of urgency with eye gaze and observed that consecutive occurrences of higher sense of urgency were prone to increase overall workload. Finally, we trained five machine learning (ML) models and showed that four of them had similar accuracy in cognitive state classification (with one, random forest, showing inferior performance). The results provided evidence that the PCPS is a reliable physiological marker for cognitive state estimation.

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“…Therefore, researchers have shown the need for building group models (across participants) for fNIRS data classification [48,49], which can enable researchers to get a larger dataset for model training and achieve more reliable results, as well as reduce the time for collecting brain data from a particular individual. However, due to inter-subject variability in hemodynamic responses, it is difficult to build robust models across participants based on fNIRS data [50][51][52][53].…”

Section: Individual Vs Group Modelsmentioning

confidence: 99%

Unsupervised fNIRS feature extraction with CAE and ESN autoencoder for driver cognitive load classification

et al. 2021

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Objective. Understanding the cognitive load of drivers is crucial for road safety. Brain sensing has the potential to provide an objective measure of driver cognitive load. We aim to develop an advanced machine learning framework for classifying driver cognitive load using functional near-infrared spectroscopy (fNIRS). Approach. We conducted a study using fNIRS in a driving simulator with the N-back task used as a secondary task to impart structured cognitive load on drivers. To classify different driver cognitive load levels, we examined the application of convolutional autoencoder (CAE) and Echo State Network (ESN) autoencoder for extracting features from fNIRS. Main results. By using CAE, the accuracies for classifying two and four levels of driver cognitive load with the 30 s window were 73.25% and 47.21%, respectively. The proposed ESN autoencoder achieved state-of-art classification results for group-level models without window selection, with accuracies of 80.61% and 52.45% for classifying two and four levels of driver cognitive load. Significance. This work builds a foundation for using fNIRS to measure driver cognitive load in real-world applications. Also, the results suggest that the proposed ESN autoencoder can effectively extract temporal information from fNIRS data and can be useful for other fNIRS data classification tasks.

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fNIRS-based classification of mind-wandering with personalized window selection for multimodal learning interfaces

Cited by 12 publications

References 63 publications

Exploring the Brain Activity Related to Missing Penalty Kicks: An fNIRS Study

Exploring the Brain Activity Related to Missing Penalty Kicks: An fNIRS Study

Assessing the Effects of Various Physiological Signal Modalities on Predicting Different Human Cognitive States

Unsupervised fNIRS feature extraction with CAE and ESN autoencoder for driver cognitive load classification

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