Abstract
This paper investigates different techniques for binary classification in a multi-participant setting, with a focus on complex movement tasks. It uses statistical methods to extract features from pre-processed biosignals acquired by functional near-infrared spectroscopy (fNIRS) from real participants, obtained from the validated finger-tapping dataset. Unique approaches are used to process the fNIRS signals, including attenuation of short channel contributions and various filtering and other pre-processing techniques. For this investigation, a number of algorithms are used to optimise hyperparameters and model topologies in six different models: four conventional machine learning methods and two artificial neural networks. Among these models, the support vector machine classifier emerges as the top performer, achieving the highest average accuracy, precision, recall and F1-score (89.17%, 91.44%, 86.67% and 88.92%, respectively). However, the multi-layer perceptron classifier shows superior performance in terms of area under the ROC curve (92.56%), closely followed by the convolutional neural network classifier (91.70%), suggesting their slightly better ability to discriminate between classes. This study highlights the potential of using different classification methods to improve the accuracy of biosignal analysis obtained from fNIRS devices.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abadi, M., et al.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems (2015). https://www.tensorflow.org/
Brigadoi, S., et al.: Motion artifacts in functional near-infrared spectroscopy: a comparison of motion correction techniques applied to real cognitive data. Neuroimage 85, 181–191 (2014)
Chollet, F.: Deep Learning with Python, 2nd edn. Manning (2021)
Cui, X., Bray, S., Reiss, A.L.: Functional near infrared spectroscopy (NIRS) signal improvement based on negative correlation between oxygenated and deoxygenated hemoglobin dynamics. Neuroimage 49(4), 3039–3046 (2010)
Delpy, D.T., Cope, M., van der Zee, P., Arridge, S., Wray, S., Wyatt, J.: Estimation of optical pathlength through tissue from direct time of flight measurement. Phys. Med. Biol. 33(12), 1433 (1988)
Fishburn, F.A., Ludlum, R.S., Vaidya, C.J., Medvedev, A.V.: Temporal derivative distribution repair (TDDR): a motion correction method for fNIRS. Neuroimage 184, 171–179 (2019)
García-Martínez, B., Fernández-Caballero, A., Martínez-Rodrigo, A., Alcaraz, R., Novais, P.: Evaluation of brain functional connectivity from electroencephalographic signals under different emotional states. Int. J. Neural Syst. 32(10), 2250026 (2022)
Gramfort, A., et al.: MEG and EEG data analysis with MNE-Python. Front. Neurosci. 7(267), 1–13 (2013)
Górriz, J., et al.: Computational approaches to explainable artificial intelligence: advances in theory, applications and trends. Inf. Fusion 100, 101945 (2023)
Khan, H., Noori, F.M., Yazidi, A., Uddin, M.Z., Khan, M.N.A., Mirtaheri, P.: Classification of individual finger movements from right hand using fNIRS signals. Sensors 21(23), 7943 (2021)
Lisboa, I.C., Queirós, S., Miguel, H., Sampaio, A., Santos, J.A., Pereira, A.F.: Infants’ cortical processing of biological motion configuration-a fNIRS study. Infant Behav. Dev. 60, 101450 (2020)
Luke, R., McAlpine, D.: fNIRS Finger Tapping Data in BIDS Format (2022). https://doi.org/10.5281/zenodo.6575155
Patashov, D., Menahem, Y., Gurevitch, G., Kameda, Y., Goldstein, D., Balberg, M.: fNIRS: non-stationary preprocessing methods. Biomed. Signal Process. Control 79, 104110 (2023)
Pedregosa, F., et al.: Scikit-Learn: Machine Learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Pollonini, L., Olds, C., Abaya, L., Bortfeld, H., Beauchamp, M.S., Oghalai, J.S.: Phoebe: a method for real time mapping of optodes-scalp coupling in functional near-infrared spectroscopy. Biomed. Opt. Express 7(12), 5104–5119 (2016)
Sánchez-Reolid, D., Sánchez-Reolid, R., Fernández-Caballero, A., Borja, A.L.: Pleasure and displeasure identification from fNIRS signals. In: Novais, P., et al. (eds.) Ambient Intelligence—Software and Applications—14th International Symposium on Ambient Intelligence, pp. 209–219. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-43461-7_21
Sánchez-Reolid, R., et al.: Artificial neural networks to assess emotional states from brain-computer interface. Electronics 7(12), 384 (2018)
Sánchez-Reolid, R., Martínez-Rodrigo, A., López, M.T., Fernández-Caballero, A.: Deep support vector machines for the identification of stress condition from electrodermal activity. Int. J. Neural Syst. 30(7), 2050031 (2020)
Sánchez-Reolid, R., et al.: Emotion classification from EEG with a low-cost BCI versus a high-end equipment. Int. J. Neural Syst. 32(10), 2250041 (2022)
Sánchez-Reolid, R., et al.: Machine learning techniques for arousal classification from electrodermal activity: a systematic review. Sensors 22, 8886 (2022)
Scholkmann, F., Metz, A.J., Wolf, M.: Measuring tissue hemodynamics and oxygenation by continuous-wave functional near-infrared spectroscopy-how robust are the different calculation methods against movement artifacts? Physiol. Meas. 35(4), 717 (2014)
Shibu, C.J., Sreedharan, S., Arun, K., Kesavadas, C., Sitaram, R.: Explainable artificial intelligence model to predict brain states from fNIRS signals. Front. Hum. Neurosci. 16 (2023).https://doi.org/10.3389/fnhum.2022.1029784
Vicente-Querol, M.A., et al.: Effect of action units, viewpoint and immersion on emotion recognition using dynamic virtual faces. Int. J. Neural Syst. 33(10), 2350053 (2023)
Vicente-Querol, M.A., et al.: Facial affect recognition in immersive virtual reality: where is the participant looking? Int. J. Neural Syst. 32(10), 2250029 (2022)
Wang, Y., et al.: A systematic review on affective computing: emotion models, databases, and recent advances. Inf. Fusion 83–84, 19–52 (2022)
Yücel, M.A., et al.: Best practices for fNIRS publications. Neurophotonics 8(1), 012101 (2021)
Acknowledgements
Grants PID2023-149753OB-C21 and PID2023-149753OB-C22 funded by Spanish MCIU/AEI/10.13039/501100011033/ERDF, EU. Grants PID2020-115220 RB-C21 and PID2020-115220RB-C22 funded by Spanish MCIN/AEI/10.13039/ 501100011033 and by “ERDF A way to make Europe”. Grant BES-2021-097834 funded by MCIN/AEI/ 10.13039/501100011033 and by “ESF Investing in your future”. Grant 2022-GRIN-34436 funded by Universidad de Castilla-La Mancha and by “ERDF A way of making Europe”. This work has also been partially supported by Junta de Comunidades de Castilla-La Mancha/ESF (grant no. SBPLY/21/180501/000030) and by CIBERSAM, Instituto de Salud Carlos III, Ministerio de Ciencia, Innovación y Universidades.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Sánchez-Reolid, D., Sánchez-Reolid, R., Gómez-Sirvent, J.L., Borja, A.L., Ferrández, J.M., Fernández-Caballero, A. (2024). Binary Classification Methods for Movement Analysis from Functional Near-Infrared Spectroscopy Signals. In: Ferrández Vicente, J.M., Val Calvo, M., Adeli, H. (eds) Artificial Intelligence for Neuroscience and Emotional Systems. IWINAC 2024. Lecture Notes in Computer Science, vol 14674. Springer, Cham. https://doi.org/10.1007/978-3-031-61140-7_38
Download citation
DOI: https://doi.org/10.1007/978-3-031-61140-7_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-61139-1
Online ISBN: 978-3-031-61140-7
eBook Packages: Computer ScienceComputer Science (R0)