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Switch scanning applications The participant was instructed to select an experimenter-cued graphical button (a) or to spell the sentence prompt (pale gray text) (b) by timing his clicks to the appropriate highlighted row or column during the switch scanning cycle. For a detailed description of (a) and (b), refer to Supplementary Figs. 8 and 9, respectively.
Source publication
Background
Brain-computer interfaces (BCIs) can restore communication for movement- and/or speech-impaired individuals by enabling neural control of computer typing applications. Single command click detectors provide a basic yet highly functional capability.
Methods
We sought to test the performance and long-term stability of click decoding using...
Citations
... For the past few decades, a major focus of the BCI field has been decoding neural activity associated with attempted movements to control a computer cursor. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] By controlling a computer cursor with their neural signals, a person with paralysis can type sentences using an on-screen keyboard, send emails and text messages, or use a web browser and many other software outcomes; instead, it describes scientific and engineering discoveries that were made using data collected in the context of the ongoing clinical trial. ...
Decoding neural activity from ventral (speech) motor cortex is known to enable high-performance speech brain-computer interface (BCI) control. It was previously unknown whether this brain area could also enable computer control via neural cursor and click, as is typically associated with dorsal (arm and hand) motor cortex. We recruited a clinical trial participant with ALS and implanted intracortical microelectrode arrays in ventral precentral gyrus (vPCG), which the participant used to operate a speech BCI in a prior study. We developed a cursor BCI driven by the participant's vPCG neural activity, and evaluated performance on a series of target selection tasks. The reported vPCG cursor BCI enabled rapidly-calibrating (40 seconds), accurate (2.90 bits per second) cursor control and click. The participant also used the BCI to control his own personal computer independently. These results suggest that placing electrodes in vPCG to optimize for speech decoding may also be a viable strategy for building a multi-modal BCI which enables both speech-based communication and computer control via cursor and click.
... Brain activity can be measured using several techniques such as electroencephalography, magnetoencephalography (MEG), and electrocorticography (ECoG) [4][5][6]. The electroencephalogram (EEG) is used as the input in most BCI systems. ...
Background
To enhance the information transfer rate (ITR) of a steady-state visual evoked potential (SSVEP)-based speller, more characters with flickering symbols should be used. Increasing the number of symbols might reduce the classification accuracy. A hybrid brain-computer interface (BCI) improves the overall performance of a BCI system by taking advantage of two or more control signals. In a simultaneous hybrid BCI, various modalities work with each other simultaneously, which enhances the ITR.
Methods
In our proposed speller, simultaneous combination of electromyogram (EMG) and SSVEP was applied to increase the ITR. To achieve 36 characters, only nine stimulus symbols were used. Each symbol allowed the selection of four characters based on four states of muscle activity. The SSVEP detected which symbol the subject was focusing on and the EMG determined the target character out of the four characters dedicated to that symbol. The frequency rate for character encoding was applied in the EMG modality and latency was considered in the SSVEP modality. Online experiments were carried out on 10 healthy subjects.
Results
The average ITR of this hybrid system was 96.1 bit/min with an accuracy of 91.2%. The speller speed was 20.9 char/min. Different subjects had various latency values. We used an average latency of 0.2 s across all subjects. Evaluation of each modality showed that the SSVEP classification accuracy varied for different subjects, ranging from 80% to 100%, while the EMG classification accuracy was approximately 100% for all subjects.
Conclusions
Our proposed hybrid BCI speller showed improved system speed compared with state-of-the-art systems based on SSVEP or SSVEP-EMG, and can provide a user-friendly, practical system for speller applications.
