---
# System prepended metadata

title: SSVEP Keypad

---

# SSVEP Experiment 
## Experiment Setup 


![](https://hackmd.io/_uploads/HyTtOT7b6.png)


Sample of Setup in time: 

![](https://hackmd.io/_uploads/HJ5o56mZa.png)


Keypad setup: 

![](https://hackmd.io/_uploads/Hkm6OTmZa.png)

## 정우 

### CCA Report 

#### Confusion Matrix 
![](https://hackmd.io/_uploads/H1EJ22mWT.png)

#### Report 
              precision    recall  f1-score   support

           0       1.00      1.00      1.00        10
           1       1.00      1.00      1.00        10
           2       1.00      1.00      1.00        10
           3       1.00      1.00      1.00        10
           4       1.00      1.00      1.00        10
           5       1.00      1.00      1.00        10
           6       1.00      1.00      1.00        10
           7       1.00      1.00      1.00        10
           8       1.00      1.00      1.00        10
           9       1.00      1.00      1.00        10
          10       1.00      1.00      1.00        10
          11       1.00      1.00      1.00        10

    accuracy                           1.00       120
   macro avg       1.00      1.00      1.00       120
weighted avg       1.00      1.00      1.00       120


#### Average Canonical Correlation Rho

![](https://hackmd.io/_uploads/BytZ2nmZ6.png)






## 지미 
#### Confusion Matrix 
![](https://hackmd.io/_uploads/SJI9Ah7Wp.png)
#### Average Canonical Correlation Rho

![](https://hackmd.io/_uploads/S1G9RhmZ6.png)


#### Report 

              precision    recall  f1-score   support

           0       1.00      1.00      1.00        10
           1       1.00      1.00      1.00        10
           2       1.00      1.00      1.00        10
           3       1.00      1.00      1.00        10
           4       1.00      1.00      1.00        10
           5       1.00      1.00      1.00        10
           6       1.00      1.00      1.00        10
           7       1.00      1.00      1.00        10
           8       1.00      1.00      1.00        10
           9       1.00      1.00      1.00        10
          10       1.00      1.00      1.00        10
          11       1.00      1.00      1.00        10

    accuracy                           1.00       120
   macro avg       1.00      1.00      1.00       120
weighted avg       1.00      1.00      1.00       120


# 정희 

![](https://hackmd.io/_uploads/rJAC1amWa.png)
![](https://hackmd.io/_uploads/HJW1lTXWp.png)




              precision    recall  f1-score   support

           0       0.62      1.00      0.77        10
           1       0.57      0.40      0.47        10
           2       0.44      0.80      0.57        10
           3       0.75      0.60      0.67        10
           4       0.50      0.70      0.58        10
           5       0.00      0.00      0.00        10
           6       0.67      0.80      0.73        10
           7       0.75      0.30      0.43        10
           8       1.00      0.80      0.89        10
           9       0.56      0.50      0.53        10
          10       0.45      0.50      0.48        10
          11       0.64      0.70      0.67        10

    accuracy                           0.59       120
   macro avg       0.58      0.59      0.56       120
weighted avg       0.58      0.59      0.56       120


## Top-k Score 
* **Top-2 Score: 0.841**
* **Top-3 Score: 0.925** 
using the Rho (Canonical Correlation Vector)

# 판규 
![](https://hackmd.io/_uploads/rkcUWaXZa.png)
![](https://hackmd.io/_uploads/Bkv8bpQZT.png)

              precision    recall  f1-score   support

           0       1.00      1.00      1.00        10
           1       1.00      1.00      1.00        10
           2       1.00      1.00      1.00        10
           3       1.00      1.00      1.00        10
           4       1.00      1.00      1.00        10
           5       1.00      1.00      1.00        10
           6       1.00      1.00      1.00        10
           7       1.00      1.00      1.00        10
           8       1.00      1.00      1.00        10
           9       1.00      1.00      1.00        10
          10       1.00      1.00      1.00        10
          11       1.00      1.00      1.00        10

    accuracy                           1.00       120
   macro avg       1.00      1.00      1.00       120
weighted avg       1.00      1.00      1.00       120
.



Given the fMRI data $z$,  we aim to learn the reverse diffusion process formulation by: $q(X_{t-1}|X_t,z)$

Conditional information uses cross-attention heads in the attention-based UNet, where 

$$CrossAttention(Q,KV) = softmax(QK^T/d^{-2})$$
with 
$$Q = W_Q^{(i)}\phi_i(x_t),K=W_K^{(i)}\tau_{\theta}(z), V=W_v^{(i)}\tau_{\theta}(z)$$

* $\tau_{\theta}$ is the fMRI encoder with a suitable dimension projector
* $\phi_i(x_t)$ denotes intermediate values of the UNet 
* $W_Q^{(i)}, W_K^{(i)}, W_V^{(i)}$ are projector matrices with learnable parameters