fwillett / handwritingbci Goto Github PK

Dear Dr. Fwillett,

I would like to express my gratitude for sharing your code. I have some problems trying to reproduce character classification and use T-SNE to see the classification effect. I have noticed that the scatter plot generated in my replication appears to have more concentrated clusters compared to the perfect separation achieved in the image presented in your paper. Although classification can also be achieved, no matter how I debug it, it is not as remarkable as the effect in your paper.

I would greatly appreciate it if you could provide me with some insights or details regarding the character classification process that could help me improve the visualization results. Thank you very much in advance for your assistance.

Best regards,
XuQian

File "D:/SIAT/DATA/handwritingBCI-main/charSeqRNN.py", line 826, in cudnnGraphSingleLayer
rnn_cudnn.build(inputSize)
File "D:\Software\Anaconda\envs\py36\lib\site-packages\tensorflow_core\python\keras\layers\recurrent.py", line 529, in build
self.input_spec[0] = get_input_spec(input_shape)
File "D:\Software\Anaconda\envs\py36\lib\site-packages\tensorflow_core\python\keras\layers\recurrent.py", line 508, in get_input_spec
input_spec_shape[time_step_index] = None
IndexError: list assignment index out of range

when I excute this code(charSeqRNN.py),I got the bug.how can I sovle this problem,thx !!!

Hi Francis,

I have a question about testing RNNmodel by unlabeled data. Instead of dividing dataset to training and validating parts, how to use a model to predict the typing result for unlabeled data? I checked the inference part and found for those validation data, it has label generated in step2. However, if the patient does online testing, we do not know what sentence the patient is writing, so it could not be labeled. Thank you very much!

Best regards,
Dongming

Hi Frank，

I have a question about warping function shown in step1 and I would appreciate it if you could help me.

Take 'W' for example. the warping function shows that the clock time and aligned time have little difference at the beginning and big difference in the end. I am confused by this phenomenon since after alignment, raw data would nonlinearly map to the template and the start time should change as the same level as end time. Could you please help me with this confusion? Thanks!

Best regards,
Dongming

Hi Francis,

I have two questions about some details in your paper High-performance brain-to-text communication via handwriting and I would appreciate it if you could help me.

The first question is about the reference electrode position. I checked the paper as well as the supplement materials but could not find the information about reference electrode. I wonder if the device has a reference electrode and if so, where it should be placed.

The second question is about the first step "time-warping" and it contains two parts. On Github, the aligned figure of trials of each single letter looks like a spike. My first part of question is about the x-axis of the figure. Does it mean 200 time bin and each bin is 10ms? If so, here comes my second part of question, is the 2-second trial signal real neural spiking or just the first compoent of PCA, but just looks like a spiking signal. According to my knowledge, the neural spiking signal should be much shorter than 2 seconds, 2 ms maybe.

Thank you very much! I am looking forward for your reply!

Best regards,

Dongming

what is the GPU memory size used in your experiment？
now， I am trying to train the RNN model with your GitHub code on GPU 16G， but I encounter an error “Fail to find the dnn implementation.” Then I operate the same experiment with the GPU 24G platform but a similar error appeared:
"(0) Internal: Blas xGEMMBatched launch failed : a.shape=[2,7500,192], b.shape=[2,192,192], m=7500, n=192, k=192, batch_size=2[[{{node MatMul}}]] [[GatherV2/_43]] "