Cerebral Hemodynamics Measured by Near-Infrared Spectroscopy at Rest and During Motor Activation

Abstract

Near-infrared spectroscopy and imaging are powerful tools to detect and continuously monitor the cerebral hemodynamic and oxygenation changes induced by brain activity. However, in addition to the focal neuronal-activation-induced hemodynamic signals, near-infrared methods are also sensitive to the cerebral hemodynamic fluctuations of systemic origin associated, for instance, with the arterial pulse, respiration, and heart rate fluctuations. We have used near-infrared spectroscopy to non-invasively measure the cerebral hemodynamics in a human subject at rest. We have observed hemoglobin oscillations at the heart rate (~1 Hz), respiratory rate (~0.2 Hz), and at lower frequencies that are associated with heart rate fluctuations and vasomotion activity. With near-infrared imaging, we have measured the spatial maps of temporal changes in the cerebral oxy- and deoxy- hemoglobin concentrations during motor activity (hand tapping). We have found a more localized activation-induced response in the deoxy-hemoglobin map with respect to the oxy-hemoglobin map. This result can be explained by the observed synchronization between the sequence of tapping/rest periods and several systemic physiological oscillation such as the arterial pulse, respiration, and heart rate fluctuations.

Towards a system-paced near-infrared spectroscopy brain–computer interface: differentiating prefrontal activity due to mental arithmetic and mental singing from the no-control state

“…To mitigate physiological noise due, primarily, to respiration (0.2-0.3 Hz) [23], cardiac activity (0.8-1.2 Hz) and the Mayer wave (approximately 0.1 Hz) [24], the raw ac light intensity signals for each period were low-pass filtered. The third-order Chebyshev-type II filter used was designed to have cut-off frequency at 0.1 Hz, stop frequency at 0.5 Hz, pass-band loss at no more than 6 dB and at least 50 dB of attenuation in the stop-band.…”

Classification of prefrontal activity due to mental arithmetic and music imagery using hidden Markov models and frequency domain near-infrared spectroscopy

“…Prior to classification, the raw ac light intensity signals were low-pass filtered to mitigate physiological noise due, primarily, to respiration (0.2-0.3 Hz) [3], cardiac signals (0.8-1.2 Hz) and the Mayer wave (approximately 0.1 Hz) [51]. We employed a wavelet filter that performed a ten-level decomposition using a Daubechies-12 wavelet.…”

“…Though NIRS has been used extensively in various other applications to assess functional brain activity [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18], its potential as a basis for BCI technologies has only recently been explored. Initial studies involving able-bodied participants focused on the use of motor imagery as the paradigm of BCI control [19][20][21].…”

“…Initial studies involving able-bodied participants focused on the use of motor imagery as the paradigm of BCI control [19][20][21]. Because motor cortical activation due to motor tasks had been investigated with positive results in other NIRS studies [3][4][5][6][7][8][9][10][11] and because motor imagery had been shown to work well in previous BCIs based on electroencephalography (EEG) [22,23], this form of brain activation was appealing for NIRS-BCI development.…”