Exploring Machine Learning Approaches for Classifying Mental Workload using fNIRS Data from HCI Tasks

Functional Near-Infrared Spectroscopy (fNIRS) has shown promise for being potentially more suitable (than e.g. EEG) for brain-based Human Computer Interaction (HCI). While some machine learning approaches have been used in prior HCI work, this paper explores different approaches and configurations for classifying Mental Workload (MWL) from a continuous HCI task, to identify and understand potential limitations and data processing decisions. In particular, we investigate three overall approaches: a logistic regression method, a supervised shallow method (SVM), and a supervised deep learning method (CNN). We examine personalised and gen-eralised models, as well as consider different features and ways of labelling the data. Our initial explorations show that generalised models can perform as well as personalised ones and that deep learning can be a suitable approach for medium size datasets. To provide additional practical advice for future brain-computer interaction systems, we conclude by discussing the limitations and data-preparation needs of different machine learning approaches. We also make recommendations for avenues of future work that are most promising for the machine learning of fNIRS data

Application of artificial intelligence in cognitive load analysis using functional near-infrared spectroscopy:A systematic review

Cognitive load theory suggests that overloading of working memory may negatively affect the performance of human in cognitively demanding tasks. Evaluation of cognitive load is a difficult task; it is often assessed through feedback and evaluation from experts. Cognitive load classification based on Functional Near-InfraRed Spectroscopy (fNIRS) is now one of the key research areas in recent years, due to its resistance of artefacts, cost-effectiveness, and portability. To make fNIRS more practical in various applications, it is necessary to develop robust algorithms that can automatically classify fNIRS signals and less reliant on trained signals. Many of the analytical tools used in cognitive sciences have used Deep Learning (DL) modalities to uncover relevant information for mental workload classification. This review investigates the research questions on the design and overall effectiveness of DL as well as its key characteristics. We have identified 45 studies published between 2011 and 2023, that specifically proposed Machine Learning (ML) models for classifying cognitive load using data obtained from fNIRS devices. Those studies were analyzed based on type of feature selection methods, input, and DL model architectures. Most of the existing cognitive load studies are based on ML algorithms, which follow signal filtration and hand-crafted features. It is observed that hybrid DL architectures that integrate convolution and LSTM operators performed significantly better in comparison with other models. However, DL models especially hybrid models have not been extensively investigated for the classification of cognitive load captured by fNIRS devices. The current trends and challenges are highlighted to provide directions for the development of DL models pertaining to fNIRS research

A LightGBM-Based EEG Analysis Method for Driver Mental States Classification

Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals and families. Recently, electroencephalography- (EEG-) based physiological and brain activities for fatigue detection have been increasingly investigated. However, how to find an effective method or model to timely and efficiently detect the mental states of drivers still remains a challenge. In this paper, we combine common spatial pattern (CSP) and propose a light-weighted classifier, LightFD, which is based on gradient boosting framework for EEG mental states identification. ,e comparable results with traditional classifiers, such as support vector machine (SVM), convolutional neural network (CNN), gated recurrent unit (GRU), and large margin nearest neighbor (LMNN), show that the proposed model could achieve better classification performance, as well as the decision efficiency. Furthermore, we also test and validate that LightFD has better transfer learning performance in EEG classification of driver mental states. In summary, our proposed LightFD classifier has better performance in real-time EEG mental state prediction, and it is expected to have broad application prospects in practical brain-computer interaction (BCI)

Signal Processing Combined with Machine Learning for Biomedical Applications

The Master’s thesis is comprised of four projects in the realm of machine learning and signal processing. The abstract of the thesis is divided into four parts and presented as follows, Abstract 1: A Kullback-Leibler Divergence-Based Predictor for Inter-Subject Associative BCI. Inherent inter-subject variability in sensorimotor brain dynamics hinders the transferability of brain-computer interface (BCI) model parameters across subjects. An individual training session is essential for effective BCI control to compensate for variability. We report a Kullback-Leibler Divergence (KLD)-based predictor for inter-subject associative BCI. An online dataset comprising left/right hand, both feet, and tongue motor imagery tasks was used to show correlation between the proposed inter-subject predictor and BCI performance. Linear regression between the KLD predictor and BCI performance showed a strong inverse correlation (r = -0.62). The KLD predictor can act as an indicator for generalized inter-subject associative BCI designs. Abstract 2: Multiclass Sensorimotor BCI Based on Simultaneous EEG and fNIRS. Hybrid BCI (hBCI) utilizes multiple data modalities to acquire brain signals during motor execution (ME) tasks. Studies have shown significant enhancements in the classification of binary class ME-hBCIs; however, four-class ME-hBCI classification is yet to be done using multiclass algorithms. We present a quad-class classification of ME-hBCI tasks from simultaneous EEG-fNIRS recordings. Appropriate features were extracted from EEG-fNIRS signals and combined for hybrid features and classified with support vector machine. Results showed a significant increase in hybrid accuracy over single modalities and show hybrid method’s performance enhancement capability. Abstract 3: Deep Learning for Improved Inter-Subject EEG-fNIRS Hybrid BCI Performance. Multimodality based hybrid BCI has become famous for performance improvement; however, the inherent inter-subject and inter-session variation between participants brain dynamics poses obstacles in achieving high performance. This work presents an inter-subject hBCI to classify right/left-hand MI tasks from simultaneous EEG-fNIRS recordings of 29 healthy subjects. State-of-art features were extracted from EEG-fNIRS signals and combined for hybrid features, and finally, classified using deep Long short-term memory classifier. Results showed an increase in the inter-subject performance for the hybrid system while making the system more robust to brain dynamics change and hints to the feasibility of EEG-fNIRS based inter-subject hBCI. Abstract 4: Microwave Based Glucose Concentration Classification by Machine Learning. Non-invasive blood sugar measurement attracts increased attention in recent years, given the increase in diabetes-related complications and inconvenience in the traditional ways using blood. This work utilized machine learning (ML) algorithms to classify glucose concentration (GC) from the measured broadband microwave scattering signals (S11). An N-type microwave adapter pair was utilized to measure the sweeping frequency scattering-parameter (S-parameter) of the glucose solutions with GC varying from 50-10,000 dg/dL. Dielectric parameters were retrieved from the measured wideband complex S-parameters based on the modified Debye dielectric dispersion model. Results indicate that the best algorithm can achieve a perfect classification accuracy and suggests an alternate way to develop a GC detection method using ML algorithms

Speech Processes for Brain-Computer Interfaces

Speech interfaces have become widely used and are integrated in many applications and devices. However, speech interfaces require the user to produce intelligible speech, which might be hindered by loud environments, concern to bother bystanders or the general in- ability to produce speech due to disabilities. Decoding a usera s imagined speech instead of actual speech would solve this problem. Such a Brain-Computer Interface (BCI) based on imagined speech would enable fast and natural communication without the need to actually speak out loud. These interfaces could provide a voice to otherwise mute people. This dissertation investigates BCIs based on speech processes using functional Near In- frared Spectroscopy (fNIRS) and Electrocorticography (ECoG), two brain activity imaging modalities on opposing ends of an invasiveness scale. Brain activity data have low signal- to-noise ratio and complex spatio-temporal and spectral coherence. To analyze these data, techniques from the areas of machine learning, neuroscience and Automatic Speech Recog- nition are combined in this dissertation to facilitate robust classification of detailed speech processes while simultaneously illustrating the underlying neural processes. fNIRS is an imaging modality based on cerebral blood flow. It only requires affordable hardware and can be set up within minutes in a day-to-day environment. Therefore, it is ideally suited for convenient user interfaces. However, the hemodynamic processes measured by fNIRS are slow in nature and the technology therefore offers poor temporal resolution. We investigate speech in fNIRS and demonstrate classification of speech processes for BCIs based on fNIRS. ECoG provides ideal signal properties by invasively measuring electrical potentials artifact- free directly on the brain surface. High spatial resolution and temporal resolution down to millisecond sampling provide localized information with accurate enough timing to capture the fast process underlying speech production. This dissertation presents the Brain-to- Text system, which harnesses automatic speech recognition technology to decode a textual representation of continuous speech from ECoG. This could allow to compose messages or to issue commands through a BCI. While the decoding of a textual representation is unparalleled for device control and typing, direct communication is even more natural if the full expressive power of speech - including emphasis and prosody - could be provided. For this purpose, a second system is presented, which directly synthesizes neural signals into audible speech, which could enable conversation with friends and family through a BCI. Up to now, both systems, the Brain-to-Text and synthesis system are operating on audibly produced speech. To bridge the gap to the final frontier of neural prostheses based on imagined speech processes, we investigate the differences between audibly produced and imagined speech and present first results towards BCI from imagined speech processes. This dissertation demonstrates the usage of speech processes as a paradigm for BCI for the first time. Speech processes offer a fast and natural interaction paradigm which will help patients and healthy users alike to communicate with computers and with friends and family efficiently through BCIs

Brain-Computer Interfaces for Non-clinical (Home, Sports, Art, Entertainment, Education, Well-being) Applications

HCI researchers interest in BCI is increasing because the technology industry is expanding into application areas where efficiency is not the main goal of concern. Domestic or public space use of information and communication technology raise awareness of the importance of affect, comfort, family, community, or playfulness, rather than efficiency. Therefore, in addition to non-clinical BCI applications that require efficiency and precision, this Research Topic also addresses the use of BCI for various types of domestic, entertainment, educational, sports, and well-being applications. These applications can relate to an individual user as well as to multiple cooperating or competing users. We also see a renewed interest of artists to make use of such devices to design interactive art installations that know about the brain activity of an individual user or the collective brain activity of a group of users, for example, an audience. Hence, this Research Topic also addresses how BCI technology influences artistic creation and practice, and the use of BCI technology to manipulate and control sound, video, and virtual and augmented reality (VR/AR)

A Novel Method for Classifying Driver Mental Workload Under Naturalistic Conditions With Information From Near-Infrared Spectroscopy

Driver cognitive distraction is a critical factor in road safety, and its evaluation, especially under real conditions, presents challenges to researchers and engineers. In this study, we considered mental workload from a secondary task as a potential source of cognitive distraction and aimed to estimate the increased cognitive load on the driver with a four-channel near-infrared spectroscopy (NIRS) device by introducing a machine-learning method for hemodynamic data. To produce added cognitive workload in a driver beyond just driving, two levels of an auditory presentation n-back task were used. A total of 60 experimental data sets from the NIRS device during two driving tasks were obtained and analyzed by machine-learning algorithms. We used two techniques to prevent overfitting of the classification models: (1) k-fold cross-validation and principal-component analysis, and (2) retaining 25% of the data (testing data) for testing of the model after classification. Six types of classifier were trained and tested: decision tree, discriminant analysis, logistic regression, the support vector machine, the nearest neighbor classifier, and the ensemble classifier. Cognitive workload levels were well classified from the NIRS data in the cases of subject-dependent classification (the accuracy of classification increased from 81.30 to 95.40%, and the accuracy of prediction of the testing data was 82.18 to 96.08%), subject 26 independent classification (the accuracy of classification increased from 84.90 to 89.50%, and the accuracy of prediction of the testing data increased from 84.08 to 89.91%), and channel-independent classification (classification 82.90%, prediction 82.74%). NIRS data in conjunction with an artificial intelligence method can therefore be used to classify mental workload as a source of potential cognitive distraction in real time under naturalistic conditions; this information may be utilized in driver assistance systems to prevent road accidents