A Brain-Computer Interface Based on Bilateral Transcranial Doppler Ultrasound

Abstract

In this study, we investigate the feasibility of a BCI based on transcranial Doppler ultrasound (TCD), a medical imaging technique used to monitor cerebral blood flow velocity. We classified the cerebral blood flow velocity changes associated with two mental tasks - a word generation task, and a mental rotation task. Cerebral blood flow velocity was measured simultaneously within the left and right middle cerebral arteries while nine able-bodied adults alternated between mental activity (i.e. word generation or mental rotation) and relaxation. Using linear discriminant analysis and a set of time-domain features, word generation and mental rotation were classified with respective average accuracies of 82.9%10.5 and 85.7%10.0 across all participants. Accuracies for all participants significantly exceeded chance. These results indicate that TCD is a promising measurement modality for BCI research.

Pattern classification to optimize the performance of Transcranial Doppler Ultrasonography-based brain machine interface

“…These are summarized in table 1. The same types of features have been previously considered in TCD lateralization studies (Aleem and Chau, 2013;Faress and Chau, 2013;Lu et al, 2014;Myrden et al, 2011).…”

“…The raw data were processed using a 3 rd order low-pass Butterworth filter with a cutoff frequency of 0.6 Hz to suppress the effects of beat-to-beat fluctuations in CBFV (Myrden et al, 2011). The filtered data were then segmented into activation and rest tasks according to time stamps embedded within the TCD recordings.…”

“…The Fisher criterion (Foley and Sammon, 1975) has been invoked in previous TCD-BCI studies and thus was considered as the baseline standard (Myrden et al, 2011). For a given feature, this criterion can be expressed as:…”

“…TCD-BCI studies to date have demonstrated automatic discrimination between language and spatial tasks (Aleem and Chau, 2013;Faress and Chau, 2013;Myrden et al, 2011). Myrden et al classified CBFVs corresponding to word generation and rest, and CBFVs corresponding to mental rotation and rest, achieving average accuracies up to 85% across nine able-bodied adults using 45s task periods.…”

Development of a hybrid mental spelling system combining SSVEP-based brain–computer interface and webcam-based eye tracking

“…Brain-computer interfaces (BCIs) are non-muscular communication and interaction technologies that allow these disabled individuals to communicate with the outside world using their brain signals [1]. To date, a variety of neural signals have been used with the aim of implementing practical BCI applications, such as electroencephalography (EEG) [1][2][3][4][5][6][7][8][9][10][11][12][13], magnetoencephalography (MEG) [14,15], electrocorticography [16,17], near-infrared spectroscopy [18][19][20], functional magnetic resonance imaging [21,22], and transcranial Doppler ultrasound [23,24]. In particular, the number of EEG-based BCI applications has increased markedly during the past five years [5].…”