High Information Transfer Rate Research Articles

An approach for brain-controlled prostheses based on Scene Graph Steady-State Visual Evoked Potentials

Brain control technology can restore communication between the brain and a prosthesis, and choosing a Brain-Computer Interface (BCI) paradigm to evoke electroencephalogram (EEG) signals is an essential step for developing this technology. In this paper, the Scene Graph paradigm used for controlling prostheses was proposed; this paradigm is based on Steady-State Visual Evoked Potentials (SSVEPs) regarding the Scene Graph of a subject’s intention. A mathematic model was built to predict SSVEPs evoked by the proposed paradigm and a sinusoidal stimulation method was used to present the Scene Graph stimulus to elicit SSVEPs from subjects. Then, a 2-degree of freedom (2-DOF) brain-controlled prosthesis system was constructed to validate the performance of the Scene Graph-SSVEP (SG-SSVEP)-based BCI. The classification of SG-SSVEPs was detected via the Canonical Correlation Analysis (CCA) approach.To assess the efficiency of proposed BCI system, the performances of traditional SSVEP-BCI system were compared. Experimental results from six subjects suggested that the proposed system effectively enhanced the SSVEP responses, decreased the degradation of SSVEP strength and reduced the visual fatigue in comparison with the traditional SSVEP-BCI system. The average signal to noise ratio (SNR) of SG-SSVEP was 6.31 ± 2.64 dB, versus 3.38 ± 0.78 dB of traditional-SSVEP. In addition, the proposed system achieved good performances in prosthesis control. The average accuracy was 94.58% ± 7.05%, and the corresponding high information transfer rate (IRT) was 19.55 ± 3.07 bit/min. The experimental results revealed that the SG-SSVEP based BCI system achieves the good performance and improved the stability relative to the conventional approach.

Enhancing Performance and Bit Rates in a Brain-Computer Interface System With Phase-to-Amplitude Cross-Frequency Coupling: Evidences From Traditional c-VEP, Fast c-VEP, and SSVEP Designs.

A brain–computer interface (BCI) is a channel of communication that transforms brain activity into specific commands for manipulating a personal computer or other home or electrical devices. In other words, a BCI is an alternative way of interacting with the environment by using brain activity instead of muscles and nerves. For that reason, BCI systems are of high clinical value for targeted populations suffering from neurological disorders. In this paper, we present a new processing approach in three publicly available BCI data sets: (a) a well-known multi-class (N = 6) coded-modulated Visual Evoked potential (c-VEP)-based BCI system for able-bodied and disabled subjects; (b) a multi-class (N = 32) c-VEP with slow and fast stimulus representation; and (c) a steady-state Visual Evoked potential (SSVEP) multi-class (N = 5) flickering BCI system. Estimating cross-frequency coupling (CFC) and namely δ-θ [δ: (0.5–4 Hz), θ: (4–8 Hz)] phase-to-amplitude coupling (PAC) within sensor and across experimental time, we succeeded in achieving high classification accuracy and Information Transfer Rates (ITR) in the three data sets. Our approach outperformed the originally presented ITR on the three data sets. The bit rates obtained for both the disabled and able-bodied subjects reached the fastest reported level of 324 bits/min with the PAC estimator. Additionally, our approach outperformed alternative signal features such as the relative power (29.73 bits/min) and raw time series analysis (24.93 bits/min) and also the original reported bit rates of 10–25 bits/min. In the second data set, we succeeded in achieving an average ITR of 124.40 ± 11.68 for the slow 60 Hz and an average ITR of 233.99 ± 15.75 for the fast 120 Hz. In the third data set, we succeeded in achieving an average ITR of 106.44 ± 8.94. Current methodology outperforms any previous methodologies applied to each of the three free available BCI datasets.

Highly Interactive Brain\u2013Computer Interface Based on Flicker-Free Steady-State Motion Visual Evoked Potential

Visual evoked potential-based brain–computer interfaces (BCIs) have been widely investigated because of their easy system configuration and high information transfer rate (ITR). However, the uncomfortable flicker or brightness modulation of existing methods restricts the practical interactivity of BCI applications. In our study, a flicker-free steady-state motion visual evoked potential (FF-SSMVEP)-based BCI was proposed. Ring-shaped motion checkerboard patterns with oscillating expansion and contraction motions were presented by a high-refresh-rate display for visual stimuli, and the brightness of the stimuli was kept constant. Compared with SSVEPs, few harmonic responses were elicited by FF-SSMVEPs, and the frequency energy of SSMVEPs was concentrative. These FF-SSMVEPs evoked “single fundamental peak” responses after signal processing without harmonic and subharmonic peaks. More stimulation frequencies could thus be selected to elicit more responding fundamental peaks without overlap with harmonic peaks. A 40-target online SSMVEP-based BCI system was achieved that provided an ITR up to 1.52 bits per second (91.2 bits/min), and user training was not required to use this system. This study also demonstrated that the FF-SSMVEP-based BCI system has low contrast and low visual fatigue, offering a better alternative to conventional SSVEP-based BCIs.

Wearable in‐the‐ear EEG system for SSVEP‐based brain–computer interface

Steady-state visual evoked potential (SSVEP) has been widely used in electroencephalogram (EEG)-based brain-computer interfaces (BCIs) due to its high information transfer rate (ITR) and short training time. Current methods usually measure SSVEP from electrodes on the scalp, which is an uncomfortable and time-consuming method. Furthermore, most research relies on expensive and non-portable EEG devices. To utilise BCIs in daily life, however, these are critical issues to address. Hence, a wearable EEG device for in-ear SSVEP detection is proposed. The system is 40 × 21 × 10.5 mm 3 and weighs 14.2 g, thus being light weight and wearable. Moreover, the system has a noise level of 0.11 μVrms, which is comparable with commercial EEG systems. Six subjects participated in an offline BCI experiment that consisted of six visual targets using the developed in-the-ear EEG system. The results showed a highest ITR of 11.03±4.18bits/min with an accuracy of 79.9 ± 13.1%, and the experiments demonstrated that the system can be utilised for unobtrusive monitoring of SSVEP in BCI applications.

A novel P300 BCI speller based on the Triple RSVP paradigm

A brain–computer interface (BCI) is an advanced human–machine interaction technology. The BCI speller is a typical application that detects the stimulated source-induced EEG signal to identify the expected characters of the subjects. The current mainstream matrix-based BCI speller involves two problems that remain unsolved, namely, gaze-dependent and space-dependent problems. Some scholars have designed gaze-independent and space-independent spelling systems. However, this system still cannot achieve a satisfactory information transfer rate (ITR). In this paper, we propose a novel triple RSVP speller with gaze-independent and space-independent characteristics and higher ITR. The triple RSVP speller uses rapid serial visual presentation (RSVP) paradigm, each time presents three different characters, and each character is presented three times to increase the ITR. The results of the experiments show the triple RSVP speller online average accuracy of 0.790 and average online ITR of 20.259 bit/min, where the system spelled at a speed of 10 s per character, and the stimulus presentation interface is a 90 × 195 pixel rectangle. Thus, the triple RSVP speller can be integrated into mobile smart devices (such as smartphones, smart watches, and others).

Eyelid Drive System: An Assistive Technology Employing Inductive Sensing of Eyelid Movement.

This paper presents the design, development, and validation of the eyelid drive system (EDS), an assistive technology comprising a specialized pair of glasses and millimeter-sized passive resonators, attached to the user's eyelids, that transduce eyelid movement (blinking and winking) through inductive sensing. The theory of operation and design optimization with simulations are presented. A proof-of-concept prototype EDS was constructed using a pair of nonprescription glasses and commercial-off-the-shelf components. In benchtop tests with model eyelids, the EDS demonstrated basic functionality. Initial trials were performed involving six human subjects interacting with custom designed graphical user interfaces on a computer. A group mean accuracy of 96.3% was achieved using a set of four different commands at a response rate of 3 s. A mean information transfer rate (ITR) of 56.1 b/min over all subjects was achieved with a set of six different commands at a response rate of 1.5 s. This proof-of-concept device consumes 51.6mW of power. The EDS compares favorably with related eye-interfacing assistive technologies and provides a unique combination of advantages, including high accuracy and ITR, wearability, insensitivity to lighting and noise conditions, obviation of facial electrodes, and the use of nonexaggerated gestures.

Brain response to luminance-based and motion-based stimulation using inter-modulation frequencies.

Steady state visual evoked potential (SSVEP)-based brain computer interface (BCI) has advantages of high information transfer rate (ITR), less electrodes and little training. So it has been widely investigated. However, the available stimulus frequencies are limited by brain responses. Simultaneous modulation of stimulus luminance is a novel method to resolve this problem. In this study, three experiments were devised to gain a deeper understanding of the brain response to the stimulation using inter-modulation frequencies. First, luminance-based stimulation using one to five inter-modulation frequencies was analyzed for the first time. The characteristics of the brain responses to the proposed stimulation were reported. Second, the motion-based stimulation with equal luminance using inter-modulation frequencies was also proposed for the first time. The response of the brain under these conditions were similar to that of luminance-based stimulation which can induce combination frequencies. And an elementary analysis was conducted to explain the reason of the occurrence of combination frequencies. Finally, the online test demonstrated the efficacy of our proposed two stimulation methods for BCI. The average ITRs reached 34.7836 bits/min and 39.2856 bits/min for luminance-based and motion-based stimulation respectively. This study demonstrated that the simultaneous modulation of stimulus luminance could extend to at least five frequencies to induce SSVEP and the brain response to the stimulus still maintained a certain positive correlation with luminance. And not only luminance-based stimulation, but also motion-based stimulation with equal luminance can elicit inter-modulation frequencies to effectively increase the number of targets for multi-class SSVEP.

Code-modulated visual evoked potentials using fast stimulus presentation and spatiotemporal beamformer decoding

When encoding visual targets using various lagged versions of a pseudorandom binary sequence of luminance changes, the EEG signal recorded over the viewer’s occipital pole exhibits so-called code-modulated visual evoked potentials (cVEPs), the phase lags of which can be tied to these targets. The cVEP paradigm has enjoyed interest in the brain-computer interfacing (BCI) community for the reported high information transfer rates (ITR, in bits/min). In this study, we introduce a novel decoding algorithm based on spatiotemporal beamforming, and show that this algorithm is able to accurately identify the gazed target. Especially for a small number of repetitions of the coding sequence, our beamforming approach significantly outperforms an optimised support vector machine (SVM)-based classifier, which is considered state-of-the-art in cVEP-based BCI. In addition to the traditional 60 Hz stimulus presentation rate for the coding sequence, we also explore the 120 Hz rate, and show that the latter enables faster communication, with a maximal median ITR of 172.87 bits/min. Finally, we also report on a transition effect in the EEG signal following the onset of the stimulus sequence, and recommend to exclude the first 150 ms of the trials from decoding when relying on a single presentation of the stimulus sequence.

The Role of Visual Noise in Influencing Mental Load and Fatigue in a Steady-State Motion Visual Evoked Potential-Based Brain-Computer Interface.

As a spatial selective attention-based brain-computer interface (BCI) paradigm, steady-state visual evoked potential (SSVEP) BCI has the advantages of high information transfer rate, high tolerance to artifacts, and robust performance across users. However, its benefits come at the cost of mental load and fatigue occurring in the concentration on the visual stimuli. Noise, as a ubiquitous random perturbation with the power of randomness, may be exploited by the human visual system to enhance higher-level brain functions. In this study, a novel steady-state motion visual evoked potential (SSMVEP, i.e., one kind of SSVEP)-based BCI paradigm with spatiotemporal visual noise was used to investigate the influence of noise on the compensation of mental load and fatigue deterioration during prolonged attention tasks. Changes in α, θ, θ + α powers, θ/α ratio, and electroencephalography (EEG) properties of amplitude, signal-to-noise ratio (SNR), and online accuracy, were used to evaluate mental load and fatigue. We showed that presenting a moderate visual noise to participants could reliably alleviate the mental load and fatigue during online operation of visual BCI that places demands on the attentional processes. This demonstrated that noise could provide a superior solution to the implementation of visual attention controlling-based BCI applications.

Brain‐controlled wheelchair controlled by sustained and brief motor imagery BCIs

A novel wireless dry EEG brain-controlled wheelchair with five steering behaviours is devised, which is based on two-stage control strategies combining sustained and brief motor imagery brain–computer interfaces (SB-MI-BCIs). Compared with the existing single modal brain–computer interface (S-BCI) controlled wheelchair, this new wheelchair has achieved more control commands and higher information transfer rate. Compared with their previous hybrid BCIs controlled wheelchair, subjects can control the wheelchair spontaneously and efficiently just by imagining the left hand, the right hand or both hands without any other visual attentions (e.g. P300 or SSVEP). The experimental results demonstrate that the SB-MI-BCIs can efficiently provide multiple independent control signals for wheelchair navigation.

Phase-based SSVEPs for real-time control of a motorised bed.

Brain-computer interface (BCI) systems have emerged as an augmentative technology that can provide a promising solution for individuals with motor dysfunctions and for the elderly who are experiencing muscle weakness. Steady-state visually evoked potentials (SSVEPs) are widely adopted in BCI systems due to their high speed and accuracy when compared to other BCI paradigms. In this paper, we apply combined magnitude and phase features for class discrimination in a real-time SSVEP-based BCI platform. In the proposed real-time system users gain control of a motorised bed system with seven motion commands and an idle state. Experimental results amongst eight participants demonstrate that the proposed real-time BCI system can successfully discriminate between different SSVEP signals achieving high information transfer rates (ITR) of 82.73 bits/min. The attractive features of the proposed system include noninvasive recording, simple electrode configuration, excellent BCI response and minimal training requirements.

A frequency recognition method based on multitaper spectral analysis and SNR estimation for SSVEP-based brain-computer interface.

Over the past several years, steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have attracted wide attention in the field of BCIs research due to high information transfer rate, little user training, and applicability to the majority. In conventional recognition methods for training-free SSVEP-based BCIs, the energy difference between the frequencies of electroencephalogram (EEG) background noise is usually ignored, therefore, there is a significant variance among the recognition accuracy of different stimulus frequencies. In order to improve the performance of training-free SSVEP-based BCIs system and balance the accuracy of recognition between different stimulus frequencies, a recognition method based on multitaper spectral analysis and signal-to-noise ratio estimation (MTSA-SNR) is proposed in this paper. A 40-class SSVEP public benchmark SSVEP dataset recorded from 35 subjects was used to evaluate the performance of the proposed method. Under the condition of 2.25s data length, the accuracy of the three methods were 81.1% (MTSA-SNR), 74.5% (canonical correlation analysis, CCA) and 73.4% (multivariate synchronization index, MSI), and the corresponding ITRs were 101 bits/min (MTSA-SNR), 89 bits/min (CCA), 87 bits/min (MSI). In the low frequency range (8-9.8Hz), the average recognition accuracy of the three methods is 82.9% (MTSA-SNR), 82.0% (CCA), 83.3% (MSI). The average accuracy of the three methods was 78.6% (MTSA-SNR), 64.9% (CCA) and 61.8% (MSI) in the high frequency range (14-15.8Hz). According to the results, the proposed method can effectively improve the performance of training-free SSVEP-based BCI system, and balance the recognition accuracy between different stimulation frequencies.

A SSVEP Stimuli Encoding Method Using Trinary Frequency-Shift Keying Encoded SSVEP (TFSK-SSVEP).

SSVEP is a kind of BCI technology with advantage of high information transfer rate. However, due to its nature, frequencies could be used as stimuli are scarce. To solve such problem, a stimuli encoding method which encodes SSVEP signal using Frequency Shift–Keying (FSK) method is developed. In this method, each stimulus is controlled by a FSK signal which contains three different frequencies that represent “Bit 0,” “Bit 1” and “Bit 2” respectively. Different to common BFSK in digital communication, “Bit 0” and “Bit 1” composited the unique identifier of stimuli in binary bit stream form, while “Bit 2” indicates the ending of a stimuli encoding. EEG signal is acquired on channel Oz, O1, O2, Pz, P3, and P4, using ADS1299 at the sample rate of 250 SPS. Before original EEG signal is quadrature demodulated, it is detrended and then band-pass filtered using FFT-based FIR filtering to remove interference. Valid peak of the processed signal is acquired by calculating its derivative and converted into bit stream using window method. Theoretically, this coding method could implement at least 2n−1 (n is the length of bit command) stimulus while keeping the ITR the same. This method is suitable to implement stimuli on a monitor and where the frequency and phase could be used to code stimuli is limited as well as implementing portable BCI devices which is not capable of performing complex calculations.

Application of a single-flicker online SSVEP BCI for spatial navigation

A promising approach for brain-computer interfaces (BCIs) employs the steady-state visual evoked potential (SSVEP) for extracting control information. Main advantages of these SSVEP BCIs are a simple and low-cost setup, little effort to adjust the system parameters to the user and comparatively high information transfer rates (ITR). However, traditional frequency-coded SSVEP BCIs require the user to gaze directly at the selected flicker stimulus, which is liable to cause fatigue or even photic epileptic seizures. The spatially coded SSVEP BCI we present in this article addresses this issue. It uses a single flicker stimulus that appears always in the extrafoveal field of view, yet it allows the user to control four control channels. We demonstrate the embedding of this novel SSVEP stimulation paradigm in the user interface of an online BCI for navigating a 2-dimensional computer game. Offline analysis of the training data reveals an average classification accuracy of 96.9±1.64%, corresponding to an information transfer rate of 30.1±1.8 bits/min. In online mode, the average classification accuracy reached 87.9±11.4%, which resulted in an ITR of 23.8±6.75 bits/min. We did not observe a strong relation between a subject’s offline and online performance. Analysis of the online performance over time shows that users can reliably control the new BCI paradigm with stable performance over at least 30 minutes of continuous operation.

A Novel Hybrid Mental Spelling Application Based on Eye Tracking and SSVEP-Based BCI.

Steady state visual evoked potentials (SSVEPs)-based Brain-Computer interfaces (BCIs), as well as eyetracking devices, provide a pathway for re-establishing communication for people with severe disabilities. We fused these control techniques into a novel eyetracking/SSVEP hybrid system, which utilizes eye tracking for initial rough selection and the SSVEP technology for fine target activation. Based on our previous studies, only four stimuli were used for the SSVEP aspect, granting sufficient control for most BCI users. As Eye tracking data is not used for activation of letters, false positives due to inappropriate dwell times are avoided. This novel approach combines the high speed of eye tracking systems and the high classification accuracies of low target SSVEP-based BCIs, leading to an optimal combination of both methods. We evaluated accuracy and speed of the proposed hybrid system with a 30-target spelling application implementing all three control approaches (pure eye tracking, SSVEP and the hybrid system) with 32 participants. Although the highest information transfer rates (ITRs) were achieved with pure eye tracking, a considerable amount of subjects was not able to gain sufficient control over the stand-alone eye-tracking device or the pure SSVEP system (78.13% and 75% of the participants reached reliable control, respectively). In this respect, the proposed hybrid was most universal (over 90% of users achieved reliable control), and outperformed the pure SSVEP system in terms of speed and user friendliness. The presented hybrid system might offer communication to a wider range of users in comparison to the standard techniques.

A novel stimulation method for multi-class SSVEP-BCI using intermodulation frequencies

Objective. Steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) has been widely investigated because of its easy system configuration, high information transfer rate (ITR) and little user training. However, due to the limitations of brain responses and the refresh rate of a monitor, the available stimulation frequencies for practical BCI application are generally restricted. Approach. This study introduced a novel stimulation method using intermodulation frequencies for SSVEP-BCIs that had targets flickering at the same frequency but with different additional modulation frequencies. The additional modulation frequencies were generated on the basis of choosing desired flickering frequencies. The conventional frame-based ‘on/off’ stimulation method was used to realize the desired flickering frequencies. All visual stimulation was present on a conventional LCD screen. A 9-target SSVEP-BCI based on intermodulation frequencies was implemented for performance evaluation. To optimize the stimulation design, three approaches (C: chromatic; L: luminance; CL: chromatic and luminance) were evaluated by online testing and offline analysis. Main results. SSVEP-BCIs with different paradigms (C, L, and CL) enabled us not only to encode more targets, but also to reliably evoke intermodulation frequencies. The online accuracies for the three paradigms were 91.67% (C), 93.98% (L), and 96.41% (CL). The CL condition achieved the highest classification performance. Significance. These results demonstrated the efficacy of three approaches (C, L, and CL) for eliciting intermodulation frequencies for multi-class SSVEP-BCIs. The combination of chromatic and luminance characteristics of the visual stimuli is the most efficient way for the intermodulation frequency coding method.

Toward a reliable PWM‐based light‐emitting diode visual stimulus for improved SSVEP response with minimal visual fatigue

Steady-state visual evoked response (SSVEP) is widely used in visual-based diagnosis and applications such as brain–computer interfacing due to its high information transfer rate and the capability to activate commands through simple gaze control. However, one major impediment in using flashing visual stimulus to obtain SSVEP is eye fatigue that prevents continued long-term use preventing practical deployment. This combined with the difficulty in establishing precise pulse-width modulation (PWM) that results in poorer accuracy warrants the development of appropriate approach to solve these issues. Various studies have suggested the usage of high frequencies of visual stimulus to reduce the visual fatigue for the user but this results in poor response performance. Here, the authors study the use of extremely high duty-cycles in the stimulus in the hope of solving these constraints. Electroencephalogram data was recorded with PWM duty-cycles of 50–95% generated by a precise custom-made light-emitting diode hardware and tested ten subjects responded that increasing duty-cycles had less visual strain for all the frequency values and the SSVEP exhibited a subject-independent peak response for duty-cycle of 85%. This could pave the way for increased usage of SSVEP for practical applications.

Grouping modulation with different codes for improving performance in cVEP‐based brain–computer interfaces

A novel paradigm for building code modulated visual evoked potential (cVEP) brain–computer interfaces (BCIs) is proposed. The paradigm is based on grouping modulation of stimuli with different codes in order to increase the number of stimuli and resulting information transfer rate (ITR). In a single group, an original code and its circularly shifting codes are used for modulating 16 stimuli. To achieve good separability of stimuli, the original codes for different groups should have good autocorrelation and cross-correlation properties. A pair of Golay complementary series and an almost perfect autocorrelation sequence are used as the original codes to implement a three-group cVEP BCI with 48 targets. Four subjects participated in a simulated online experiment and a high ITR of 129.58 bits/min was achieved due to the sharp increase of the number of targets.

Polychromatic SSVEP stimuli with subtle flickering adapted to brain-display interactions

Objective. Interactive displays armed with natural user interfaces (NUIs) will likely lead the next breakthrough in consumer electronics, and brain-computer interfaces (BCIs) are often regarded as the ultimate NUI-enabling machines to respond to human emotions and mental states. Steady-state visual evoked potentials (SSVEPs) are a commonly used BCI modality due to the ease of detection and high information transfer rates. However, the presence of flickering stimuli may cause user discomfort and can even induce migraines and seizures. With the aim of designing visual stimuli that can be embedded into video images, this study developed a novel approach to induce detectable SSVEPs using a composition of red/green/blue flickering lights. Approach. Based on the opponent theory of colour vision, this study used 32 Hz/40 Hz rectangular red-green or red-blue LED light pulses with a 50% duty cycle, balanced/equal luminance and 0°/180° phase shifts as the stimulating light sources and tested their efficacy in producing SSVEP responses with high signal-to-noise ratios (SNRs) while reducing the perceived flickering sensation. Main results. The empirical results from ten healthy subjects showed that dual-colour lights flickering at 32 Hz/40 Hz with a 50% duty cycle and 180° phase shift achieved a greater than 90% detection accuracy with little or no flickering sensation. Significance. As a first step in developing an embedded SSVEP stimulus in commercial displays, this study provides a foundation for developing a combination of three primary colour flickering backlights with adjustable luminance proportions to create a subtle flickering polychromatic light that can elicit SSVEPs at the basic flickering frequency.

An N100-P300 Spelling Brain-Computer Interface with Detection of Intentional Control

A brain-computer interface (BCI) is a tool to communicate with a computer via brain signals without the user making any physical movements, thus enabling disabled people to communicate with their environment and with others. P300-based ERP spellers are a widely used spelling visual BCI using the P300 component of event-related potential (ERP). However, they have a technical problem in that at least 2 N flashes are required to present N characters. This prevents the improvement of accuracy and restricts the typing speed. To address this issue, we propose a method that uses N100 in addition to P300. We utilize novel stimulus images to detect the user’s gazing position by using N100. By using both P300 and N100, the proposed visual BCI reduces the number of flashes and improves the accuracy of the P300 speller. We also propose using N100 to classify non-control (NC) and intentional control (IC) states. In our experiments, the detection accuracy of N100 was significantly higher than that of P300 and the proposed method exhibited a higher information transfer rate (ITR) than the P300 speller.