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Enhancing Classification Performance of Functional Near-Infrared Spectroscopy- Brain-Computer Interface Using Adaptive Estimation of General Linear Model Coefficients.

ABSTRACT: In this paper, a novel methodology for enhanced classification of functional near-infrared spectroscopy (fNIRS) signals utilizable in a two-class [motor imagery (MI) and rest; mental rotation (MR) and rest] brain-computer interface (BCI) is presented. First, fNIRS signals corresponding to MI and MR are acquired from the motor and prefrontal cortex, respectively, afterward, filtered to remove physiological noises. Then, the signals are modeled using the general linear model, the coefficients of which are adaptively estimated using the least squares technique. Subsequently, multiple feature combinations of estimated coefficients were used for classification. The best classification accuracies achieved for five subjects, for MI versus rest are 79.5, 83.7, 82.6, 81.4, and 84.1% whereas those for MR versus rest are 85.5, 85.2, 87.8, 83.7, and 84.8%, respectively, using support vector machine. These results are compared with the best classification accuracies obtained using the conventional hemodynamic response. By means of the proposed methodology, the average classification accuracy obtained was significantly higher (p < 0.05). These results serve to demonstrate the feasibility of developing a high-classification-performance fNIRS-BCI.

SUBMITTER: Qureshi NK

PROVIDER: S-EPMC5512010 | biostudies-other | 2017

REPOSITORIES: biostudies-other

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Project description:Severely motor-disabled patients, such as those suffering from the so-called "locked-in" syndrome, cannot communicate naturally. They may benefit from brain-computer interfaces (BCIs) exploiting brain signals for communication and therewith circumventing the muscular system. One BCI technique that has gained attention recently is functional near-infrared spectroscopy (fNIRS). Typically, fNIRS-based BCIs allow for brain-based communication via voluntarily modulation of brain activity through mental task performance guided by visual or auditory instructions. While the development of fNIRS-BCIs has made great progress, the reliability of fNIRS-BCIs across time and environments has rarely been assessed. In the present fNIRS-BCI study, we tested six healthy participants across three consecutive days using a straightforward four-choice fNIRS-BCI communication paradigm that allows answer encoding based on instructions using various sensory modalities. To encode an answer, participants performed a motor imagery task (mental drawing) in one out of four time periods. Answer encoding was guided by either the visual, auditory, or tactile sensory modality. Two participants were tested outside the laboratory in a cafeteria. Answers were decoded from the time course of the most-informative fNIRS channel-by-chromophore combination. Across the three testing days, we obtained mean single- and multi-trial (joint analysis of four consecutive trials) accuracies of 62.5 and 85.19%, respectively. Obtained multi-trial accuracies were 86.11% for visual, 80.56% for auditory, and 88.89% for tactile sensory encoding. The two participants that used the fNIRS-BCI in a cafeteria obtained the best single- (72.22 and 77.78%) and multi-trial accuracies (100 and 94.44%). Communication was reliable over the three recording sessions with multi-trial accuracies of 86.11% on day 1, 86.11% on day 2, and 83.33% on day 3. To gauge the trade-off between number of optodes and decoding accuracy, averaging across two and three promising fNIRS channels was compared to the one-channel approach. Multi-trial accuracy increased from 85.19% (one-channel approach) to 91.67% (two-/three-channel approach). In sum, the presented fNIRS-BCI yielded robust decoding results using three alternative sensory encoding modalities. Further, fNIRS-BCI communication was stable over the course of three consecutive days, even in a natural (social) environment. Therewith, the developed fNIRS-BCI demonstrated high flexibility, reliability and robustness, crucial requirements for future clinical applicability.

Project description:BACKGROUND:Although real-time localization of gliomas has improved with intraoperative image guidance systems, these tools are limited by brain shift, surgical cavity deformation, and expense. OBJECTIVE:To propose a novel method to perform near-infrared (NIR) imaging during glioma resections based on preclinical and clinical investigations, in order to localize tumors and to potentially identify residual disease. METHODS:Fifteen patients were identified and administered a Food and Drug Administration-approved, NIR contrast agent (Second Window indocyanine green [ICG], 5 mg/kg) before surgical resection. An NIR camera was utilized to localize the tumor before resection and to visualize surgical margins following resection. Neuropathology and magnetic resonance imaging data were used to assess the accuracy and precision of NIR fluorescence in identifying tumor tissue. RESULTS:NIR visualization of 15 gliomas (10 glioblastoma multiforme, 1 anaplastic astrocytoma, 2 low-grade astrocytoma, 1 juvenile pilocytic astrocytoma, and 1 ganglioglioma) was performed 22.7 hours (mean) after intravenous injection of ICG. During surgery, 12 of 15 tumors were visualized with the NIR camera. The mean signal-to-background ratio was 9.5 ± 0.8 and fluorescence was noted through the dura to a maximum parenchymal depth of 13 mm. The best predictor of positive fluorescence was enhancement on T1-weighted imaging; this correlated with signal-to-background ratio (P = .03). Nonenhancing tumors did not demonstrate NIR fluorescence. Using pathology as the gold standard, the technique demonstrated a sensitivity of 98% and specificity of 45% to identify tumor in gadolinium-enhancing specimens (n = 71). CONCLUSION:With the use of Second Window ICG, gadolinium-enhancing tumors can be localized through brain parenchyma intraoperatively. Its utility for margin detection is promising but limited by lower specificity. ABBREVIATIONS:5-ALA, 5-aminolevulinic acidEPR, enhanced permeability and retentionFDA, Food and Drug AdministrationGBM, glioblastomaICG, indocyanine greenNIR, near-infraredNPV, negative predictive valuePPV, positive predictive valueROC, receiver operating characteristicROI, region of interestSBR, signal-to-background ratioWHO, World Health Organization.

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Enhancing Classification Performance of Functional Near-Infrared Spectroscopy- Brain-Computer Interface Using Adaptive Estimation of General Linear Model Coefficients.

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