A Brain-Computer Interface Based on Bilateral Transcranial Doppler Ultrasound

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

Detecting and Classifying Cognitive Activity Based on Changes in Cerebral Blood Flow Velocity

Individuals with severe physical impairments have a reduced ability to communicate through movement and speech. We investigated transcranial Doppler ultrasound as a potential measurement modality for a novel brain-computer interface. It was hypothesized that cognitive activity would result in detectable changes in cerebral blood flow velocity within the middle cerebral arteries. Nine able-bodied participants alternated between rest and two different mental activities - silent word generation and mental rotation. Two analyses were performed to assess the feasibility and practicality of a TCD-based brain-computer interface. Both mental activities were independently differentiated from rest with high accuracy. Intuitive time-domain features were sufficient for classification. Data transmission rate was quadrupled by differentiating all three classes simultaneously using shorter state durations. Transcranial Doppler ultrasound can be used to automatically detect cognitive activity and may be useful as the basis of a brain-computer interface.MAS

Towards Context-aware Brain-computer Interfaces

Brain-computer interfaces (BCIs) allow individuals with disabilities to communicate and control their environment without the necessity for volitional speech or motor activation. However, most current BCIs are prone to significant performance fluctuations and incapable of adapting to their users. These shortcomings impair practical BCI usage. This thesis investigates the feasibility of a hybrid BCI that is capable of detecting and adapting to underlying changes in user mental state. The thesis comprises four major studies, each representing a step towards this ultimate goal. The first study formulates a novel signal processing algorithm for the frequency-domain analysis of electroencephalographic (EEG) recordings. The second study examines the ability to automatically detect fluctuations in three mental states that are important to BCI usage - fatigue, frustration, and attention - based on electrical activity recorded from the surface of the scalp using EEG. The third study explores the effects of each of these mental states on the online operation of a two-class EEG-BCI. The final study investigates the efficacy of two different methods - reliability prediction and adaptive classification - by which a BCI can adapt to changes in fatigue, frustration, and attention. In the first study, the novel algorithm, based on a clustering of spectral power features, was shown to compress EEG signals with less information loss than traditional frequency-domain analyses. In the second study, fluctuations in fatigue, frustration, and attention were detected with mean classification accuracies of 76.8%, 71.9%, and 86.1%, respectively. In the third study, a significant relationship between perceived frustration and BCI accuracy was uncovered and optimal regions for BCI performance were identified in several multi-dimensional representations of user mental state. In the final study, estimated mental state was used to predict the onset of low-accuracy BCI performance, with an 8% decrement in classification accuracy between the predicted high and low accuracy conditions. This study also demonstrated the ability to directly adapt BCI classification, leading to statistically significant increases in classification accuracy for roughly half of participants without significantly compromising performance for the remainder of the study population.Ph.D.2018-07-08 00:00:0

Average feature selection across all participants for both tasks.

<p>The word generation task is on top, and the mental rotation task on bottom. Black bars are left MCA features, grey bars are right MCA features, and white bars are bilateral features. Bilateral features are more frequently selected for the word generation task, likely due to the left-hemispheric lateralization of this task. Feature descriptions can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024170#pone-0024170-t001" target="_blank">Table 1</a>.</p

Scatter plots showing the relationship between rest and activation for Participant 2.

<p>The word generation task is on the left, and the mental rotation task on the right. The three most frequently selected features for each task have been used to characterize each state. Rest and activation states are represented by ‘x’ and ‘o’ respectively. Both tasks are highly separable for this participant.</p

Most frequently selected features for each participant for each task (abbreviations given in Table 1).

<p>Most frequently selected features for each participant for each task (abbreviations given in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024170#pone-0024170-t001" target="_blank">Table 1</a>).</p

Classification accuracies for the word generation task.

<p>Columns two through four show accuracies when 1–3 features were selected from the entire candidate pool. Column 5 shows the accuracies when the candidate pool was restricted to the 16 bilateral features. In this case, the analysis was performed for only the selection of three features, using the same feature selection algorithm. The final column shows classification accuracies when only respiratory features were used. All participants except for Participant 8 displayed significantly higher accuracies for three-feature TCD classification (using the entire candidate pool) than for classification based on respiration. Mean classification accuracy was significantly greater for two and three features than for one feature (repeated-measures regression, p0.012). The comparison between two and three features approached significance (p = 0.056).</p

Recordings from two rest-activation cycles for participant 4.

<p>The solid line depicts CBFV in the left MCA, while the broken line depicts CBFV in the right MCA. Decreasing trends in CBFV during rest and increasing trends during activation are apparent. The signal is the mean of the maximum velocity, filtered by a Butterworth low-pass filter with a cutoff frequency of 0.6 Hz.</p

Classification accuracies for the mental rotation task.

<p>Columns two through four show accuracies when 1–3 features were selected from the entire candidate pool. Column 5 shows the accuracies when the candidate pool was restricted to the 16 bilateral features. In this case, the analysis was performed for only the selection of three features, using the same feature selection algorithm. The final column shows classification accuracies when only respiratory features were used. All participants except for Participant 6 displayed significantly higher accuracies for three-feature TCD classification (using the entire candidate pool) than for classification based on respiration. Mean classification accuracy was significantly greater for two and three features than for one feature (repeated-measures regression, p0.001). There was no significant difference between two and three features.</p