---
title: Code-Modulated Motion VEP (c-MVEP)
url: https://www.emergentmind.com/topics/code-modulated-motion-visual-evoked-potential-c-mvep
type: topic
---

# Code-Modulated Motion VEP (c-MVEP)

Code-modulated motion visual evoked potential (c-MVEP) is a visual brain-computer interfacing (BCI) paradigm in which pseudo-random sequences modulate object motion rather than luminance flicker. In the formulation introduced in "Beyond Flickering: Introducing Code-Modulated Motion Visual Evoked Potentials for Brain-Computer Interfacing," c-MVEP uses an m-sequence-driven radial zooming stimulus and is positioned as a motion-based alternative to code-modulated visual evoked potential (c-VEP), while also being compared with steady-state motion visual evoked potential (SSMVEP) and steady-state visual evoked potential (SSVEP). In offline and online experiments, c-MVEP exhibited time-domain characteristics similar to c-VEP, evoked a broadband response with comparable signal-to-noise ratio (SNR) but more concentrated in the lower frequency range, and achieved intermediate online BCI performance: lower than c-VEP and SSVEP, but higher than SSMVEP [2605.15801].

## 1. Stimulus formalization and code design

The c-MVEP stimulus is driven by a pseudo-random sequence generated by a 5-bit linear feedback shift register (LFSR) with primitive polynomial

$$
p(x) = x^5 + x^2 + 1
$$

and initial register state $\mathbf r(0)=(1,1,1,1,1)$. The sequence length is $K=2^5-1=31$ bits. The offline prototype sequence is

$$
c_0 = 0001101110101000010010110011111.
$$

For the online 4-class BCI, four circular shifts of $c_0$ by 1, 3, 5, and 8 bits were used as the four class codes. The presentation rate is $20\,\mathrm{Hz}$, so one bit lasts 18 frames on a $360\,\mathrm{Hz}$ display, with $F_b=18$ [2605.15801].

Binary transitions are smoothed to produce a continuous motion driver. The transition window length is $L=\alpha F_b$ with $\alpha=1$. A rising edge $0\to1$ is replaced by

$$
s(t)=\tfrac12\Bigl(1+\sin\!\bigl[\pi\bigl(\tfrac{t}{L}-\tfrac12\bigr)\bigr]\Bigr), \quad t=0,\dots,L,
$$

and falling edges are replaced by $1-s(t)$. The resulting continuous sequence in $[0,1]$ drives radial zooming between 50% and 100% of the original stimulus size.

The temporal structure is explicitly cycle-based. One m-sequence cycle lasts $K/20=31/20=1.55\,\mathrm{s}$. Offline stimulation uses 3 cycles, giving $4.65\,\mathrm{s}$ per trial, with an inter-trial interval of $0.5\,\mathrm{s}$. Online calibration uses the same $4.65\,\mathrm{s}$ duration per trial, whereas testing uses dynamic stopping. This design places c-MVEP within the code-modulated VEP family while replacing flicker with motion as the stimulation channel.

## 2. Electrophysiological characteristics

Offline EEG preprocessing consists of a notch filter at $50\,\mathrm{Hz}$, a bandpass of $1$–$40\,\mathrm{Hz}$ using a zero-phase 4th-order Butterworth filter, baseline correction, and epoching. For time-domain analysis, segments are cut into non-overlapping cycles $T$, yielding $\mathbf X_i \in \mathbb R^{C\times T}$. The cycle duration is $T=1.55\,\mathrm{s}$ for c-MVEP and c-VEP, and $T=1.0\,\mathrm{s}$ for SSMVEP and SSVEP [2605.15801].

At Oz, the grand-average ERP for c-MVEP shows a pronounced negative peak at approximately $1.0\,\mathrm{s}$. The c-VEP waveform differs subtly but shares broad features. By contrast, SSMVEP and SSVEP show clear sinusoidal ERPs. In frequency-domain analysis, the power spectral density (PSD) is computed using a Hamming-windowed FFT zero-padded to $10\,\mathrm{s}$, giving $\delta f=0.1\,\mathrm{Hz}$. For c-MVEP and c-VEP, $P_{\mathrm{noise}}$ is defined as the median of $\pm 15$ bins excluding the $\pm 2$ adjacent bins; for SSMVEP and SSVEP, it is the median of $\pm 20$ bins excluding the $\pm 4$ adjacent bins.

The main Oz peaks distinguish the paradigms. For c-MVEP, the peak at $20\,\mathrm{Hz}$ is $2.11\,\mathrm{dB}$; for c-VEP, the corresponding peak is $3.06\,\mathrm{dB}$. For SSMVEP, the fundamental at $5\,\mathrm{Hz}$ is $2.36\,\mathrm{dB}$, with harmonics at $10\,\mathrm{Hz}$ of $2.01\,\mathrm{dB}$ and $15\,\mathrm{Hz}$ of $1.15\,\mathrm{dB}$. For SSVEP, the fundamental at $10\,\mathrm{Hz}$ is $8.76\,\mathrm{dB}$, with harmonics at $20\,\mathrm{Hz}$ of $10.54\,\mathrm{dB}$ and $30\,\mathrm{Hz}$ of $6.86\,\mathrm{dB}$. c-MVEP and c-VEP therefore evoke broadband spectra, whereas SSMVEP and SSVEP evoke narrowband peaks at the stimulation frequency and harmonics.

Spatially, c-MVEP has peak SNR at Oz but spreads to PO$_z$, Pz, O2, and PO$_4$. c-VEP is highly focal at Oz. SSMVEP is broader over temporal-occipital areas, whereas SSVEP is confined to occipital sites. The study states that the c-MVEP pattern suggests engagement of motion-sensitive areas. A plausible implication is that c-MVEP recruits a wider cortical network than flicker-based code modulation, although the data as presented are spatial-topographic rather than source-resolved.

## 3. Detection and classification framework

The online decoder uses template-matched canonical correlation analysis (CCA). For each class, the template is defined as

$$
\bm T_i=\tfrac1R\sum_{r=1}^R\bm X_r.
$$

Trials are stacked as $\bm S=[\bm X_{y_1},\dots,\bm X_{y_J}]$ and the corresponding templates as $\bm T=[\bm T_{y_1},\dots,\bm T_{y_J}]$. The optimization is

$$
\max_{\bm w,\bm v}\mathrm{corr}(\bm w^\top\bm S,\;\bm v^\top\bm T)
=
\frac{\bm w^\top\bm S\,\bm T^\top\bm v}
{\sqrt{(\bm w^\top\!S\,S^\top\bm w)\,(\bm v^\top\!T\,T^\top\bm v)}}.
$$

The spatial filters $\bm w$ and $\bm v$ are then applied to a new trial $\bm X$ and each class template $\bm T_i$, giving $\bm x=\bm w^\top\!X$ and $\bm t_i=\bm v^\top\!T_i$. Classification is performed by

$$
\hat y
=\arg\max_i
\frac{\bm x^\top \bm t_i}{\|\bm x\|\;\|\bm t_i\|}.
$$

The paper notes that this is equivalent to seeking $\max_{w_x,w_y}\mathrm{corr}(w_x^\top X,w_y^\top Y)$, that is, time-domain CCA [2605.15801].

Dynamic stopping is integrated into online testing. The stopping criterion uses a margin $\rho_m-\rho_r\ge0.3$, with a minimum window of $0.75\,\mathrm{s}$. The window expands in $0.05\,\mathrm{s}$ steps and then slides up to a maximum of $4.65\,\mathrm{s}$ for c-MVEP and c-VEP or $5\,\mathrm{s}$ for SSMVEP and SSVEP, with an overall limit of $9.3\,\mathrm{s}$. This procedure operationalizes c-MVEP as a practical asynchronous decision process rather than a fixed-window offline decoder.

## 4. Quantitative BCI performance

Performance is reported in terms of accuracy $P$, average selection time $T$, and information transfer rate (ITR) in bits per minute:

$$
\mathrm{ITR}
=
\frac{\log_2 N + P\log_2 P + (1-P)\log_2\frac{1-P}{N-1}}{T/60},
$$

where $N=4$ classes and $T$ includes the inter-trial interval of approximately $2$–$2.5\,\mathrm{s}$ [2605.15801].

In the online 4-class BCI, the average $\pm$ standard error values are:

- **c-MVEP**: $85.67\% \pm 3.31\%$, $2.61 \pm 0.25\,\mathrm{s}$, $20.11 \pm 2.46\,\mathrm{bits/min}$
- **c-VEP**: $97.81\% \pm 0.84\%$, $1.15 \pm 0.08\,\mathrm{s}$, $35.56 \pm 1.36\,\mathrm{bits/min}$
- **SSMVEP**: $64.91\% \pm 5.32\%$, $4.18 \pm 0.37\,\mathrm{s}$, $10.13 \pm 2.41\,\mathrm{bits/min}$
- **SSVEP**: $93.42\% \pm 2.11\%$, $1.94 \pm 0.28\,\mathrm{s}$, $28.12 \pm 2.24\,\mathrm{bits/min}$

Statistical analysis uses Friedman and Wilcoxon tests with Bonferroni-adjusted $\alpha_{\mathrm{adj}}\approx .0083$. The accuracy ordering is reported as c-VEP $>$ SSVEP $\approx>$ c-MVEP $>$ SSMVEP, with c-MVEP $\gg$ SSMVEP ($p<.001$) and SSVEP $>$ c-MVEP ($p\le .002$). For selection time, the ordering is c-VEP $<$ SSVEP $<$ c-MVEP $<$ SSMVEP. For ITR, the ordering is c-VEP $>$ SSVEP $>$ c-MVEP $>$ SSMVEP.

These results place c-MVEP between the code-modulated flicker paradigm and the steady-state motion paradigm. The specific comparison reported in the abstract is that the c-MVEP BCI reached a mean accuracy of $85.67\%$ with an average selection time of $2.61\,\mathrm{s}$, which was significantly lower than c-VEP ($97.81\%$; $1.15\,\mathrm{s}$) and SSVEP ($93.42\%$; $1.94\,\mathrm{s}$), but significantly higher than SSMVEP ($64.91\%$; $4.18\,\mathrm{s}$).

## 5. Subjective assessment and user preference

The study includes both offline and online subjective evaluations. In the offline questionnaire, 9 subjects rated the paradigms on a 6-point Likert scale. No clear preference was found for motion-based stimulation, represented by c-MVEP and SSMVEP, over flicker-based stimulation, represented by c-VEP and SSVEP, across comfort, concentration, disturbance, focus loss, likability, and overall rating. The highest overall rating was reported for c-VEP at $4.22$, followed by SSVEP at $3.78$, c-MVEP at $3.33$, and SSMVEP at $3.11$ [2605.15801].

The online questionnaire involved 19 subjects and 7 questions plus a forced-choice component. No significant differences among conditions were found for fatigue, comfort, concentration, disturbance, focus loss, likability, or overall rating, with all $p\ge .073$. In the end-of-study forced choices, 11 of 19 preferred flicker for ease of focus, which was not significant; 14 of 19 stated that flicker caused more eye strain, with $p=.064$; and overall 10 of 19 preferred flicker versus 9 of 19 preferring motion, which was also not significant.

A common assumption is that motion-based stimulation necessarily confers a strong comfort advantage over flicker. The reported data do not support that claim at the tested $20\,\mathrm{Hz}$ bit-rate. The study instead reports no clear preference for motion and no significant differences on the main questionnaire dimensions, even though the forced-choice responses suggest a tendency for more eye strain under flicker.

## 6. Position within VEP-based BCI research

c-MVEP is explicitly characterized as combining the broad-band, high-SNR characteristics of c-VEP with flicker-free motion stimulation. Its time-domain and frequency-domain ERPs closely resemble c-VEP in being broad-band and m-sequence-driven, but the study states that c-MVEP has slightly lower amplitude and greater inter-subject variability [2605.15801].

The paradigm also occupies a distinct position relative to steady-state motion stimulation. SSMVEP and SSVEP are described by oscillatory responses at the stimulation frequency and harmonics, whereas c-MVEP and c-VEP are code-modulated and broadband. In practical online BCI use, c-MVEP significantly outperforms SSMVEP but remains below c-VEP and SSVEP. This suggests that substituting motion for flicker in a code-modulated framework preserves much of the characteristic response structure of c-VEP while changing both spatial distribution and attainable performance.

The spatial maps are particularly important for classification of the paradigm. c-MVEP peaks at Oz but spreads into PO$_z$, Pz, O2, and PO$_4$, while c-VEP remains highly focal at Oz; SSMVEP is broader over MT areas, and SSVEP is occipital-only. The study interprets these differences as indicating engagement of a wider cortical network for motion-based stimulation beyond primary occipital regions. A plausible implication is that decoder designs optimized for focal occipital responses may not fully exploit c-MVEP, which aligns with the paper’s suggestion that further gains may be possible through decoder optimization, including reconvolution CCA, and through motion-parameter tuning.

## 7. Significance, limitations, and prospective directions

Within the reported evidence, c-MVEP is a viable flicker-free alternative for BCI users intolerant to high-contrast flicker. That conclusion rests on several specific findings: c-MVEP successfully elicits broadband code-modulated responses; its online 4-class performance is clearly above SSMVEP; and it provides a motion-based alternative to c-VEP without requiring steady-state oscillatory entrainment [2605.15801].

At the same time, the limitations are explicit. c-MVEP does not match c-VEP in accuracy, selection time, or ITR, and it also remains below SSVEP in the online comparisons. Subjective data do not show a strong comfort or preference advantage for motion over flicker at the tested parameters. The paradigm’s broader scalp distribution and greater inter-subject variability further indicate that its current implementation may be less optimized than the established flicker-based alternatives.

The study’s concluding implications are therefore measured rather than categorical. c-MVEP is presented as having great potential and as providing a valuable alternative to c-VEP, but not as a superior replacement under the reported conditions. The paper specifically identifies decoder optimization, such as reconvolution CCA, and motion-parameter tuning as directions through which further gains may be obtained.

Source: https://www.emergentmind.com/topics/code-modulated-motion-visual-evoked-potential-c-mvep