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Decoding Auditory Information from the Human Brain Using Functional near Infrared Spectroscopy

Introduction T he present study reports the ability to decode auditory activation in the cortical region of the human brain evoked by multiple soundcategories for both offline and online classification. To that end, the haemodynamic response (HR) is measured in the leftand right-hemispheres of the auditory cortex using functional near infrared spectroscopy (fNIRS). In this article, six sound-categories (English-speech, non-English-speech, annoying sounds, nature sounds, classical music and gunshot sounds) are studied (25 subjects), and two-class (i.e., English vs non-English, annoying vs nature sounds), four-class (i.e., English, non-English, annoying and nature sounds) and six-class classification problems are discussed. The two wavelengths of the fNIRS used in this work are 760 nm and 830 nm; relevant light-absorbing chromophores are oxy-haemoglobin (HbO) and deoxy-haemoglobin (HbR), the concentrations of which are calculated using a modified Beer–Lambert law. fNIRS has several advantages over other modalities, including non-invasiveness, portability, safety and its low cost. Additionally, fNIRS offers a good trade-off between spatial and temporal resolutions compared with electroencephalography and functional magnetic resonance imaging (fMRI), respectively. Given these advantages, NIR techniques have been applied to various applications such as neurology, experimental psychology and brain research. A limitation of fNIRS, however, is the fact that the maximum light penetration depth is about 4 cm. It has an exceptional spatial resolution for monitoring the HR in the auditory cortex. Another benefit of this technique is its more silent optical neuroimaging compared with fMRI. Indeed, fMRI is relatively problematic for auditory cortex studies, as its associated mechanical noise can cause interference when measuring task-evoked brain activities. Overall, fNIRS is a very suitable technique for measuring the auditory cortex in the human brain and allows the subject either to sit in an upright position or lie down. The HRs (i.e., HbO and HbR) measured by fNIRS are combinations of various components: a task-evoked signal, a non-evoked signal (e.g., spontaneous fluctuations in about <0.01 Hz), physiological processes (e.g., respiratory and cardiac in about ~0.2– 0.3 Hz and ~1 Hz, respectively) and motion artefacts. In fNIRS data processing, such unwanted noise (excluding the task-evoked signal) needs to be reduced or removed in order to obtain higher-quality topographic images using adaptive filtering and/or the independent component analysis (ICA) algorithm. Further, the classification approach is a technique that uses feature selection to estimate data classes. For example, the mean, slope, skewness and kurtosis values of HbO and HbR signals for every trial over all channels were used as good features for classification. In this study, two classifiers in linear discriminant analysis (LDA) and support vector machines (SVMs), the most popular algorithms for classification, were applied to the multi-class problems. The aim of this study is to decode what humans are hearing by measuring the audiotask-evoked signal of the auditory activation bilaterally using fNIRS. Sounds from different sound-categories, selected from a website, were presented to 25 subjects. Pre-processing techniques (i.e., filtering), statistical analysis and the ICA algorithm were used for noise reduction in order to enhance classification accuracy. Further, the combinations of the mean, slope, skewness and kurtosis values of the HbO and HbR signals were calculated in the classification process as a features set. Finally, decoding of the multiple sound categories via multi-class LDA and SVM classifiers in offline and online schemes was investigated. This article summarises previously published work (with additional sound categories and on-line independent component analysis) to assess the use of functional near-infrared spectroscopy in decoding auditory activation for both off-line and on-line classification. Materials and methods A total of 25 subjects participated in this study: 18 subjects (28.11 ± 4.32 years) and 7 subjects (27.33 ± 2.93 years) for four and six different sound-categories, respectively. All of the subjects had normal hearing and no history of any neurological disorder. The subjects were asked to listen to various audio stimuli attentively and to guess, for each stimulus, which category was heard. The experiment was conducted in accordance with the ethical standards outlined in the latest Declaration of Helsinki. Audio stimuli consisted of six different sound-categories selected from the website www.youtube.com: two speech languages (English, non-English), two types of sounds (annoying sounds, nature sounds), classical music and gunshot sounds. Categories 1–2, 3–4, 1–4 and 1–6 were used in the two-class (speech), two-class (sound), four-class and six-class problems, respectively. Accordingly, Subjects 1–18 and 19–25 were, respectively, exposed to 24 doi: 10.1255/nirn.1608