jinglescode

class TestConv1d(nn.Module):
    def __init__(self):
        super(TestConv1d, self).__init__()
        self.conv = nn.Conv1d(in_channels=1, out_channels=1, kernel_size=3, stride=3, bias=False)
        self.init_weights()

    def forward(self, x):
        return self.conv(x)
    
    def init_weights(self):

jinglescode

class TestConv1d(nn.Module):
    def __init__(self):
        super(TestConv1d, self).__init__()
        self.conv = nn.Conv1d(in_channels=1, out_channels=1, kernel_size=5, padding=2, bias=False)
        self.init_weights()

    def forward(self, x):
        return self.conv(x)
    
    def init_weights(self):

jinglescode

class TestConv1d(nn.Module):
    def __init__(self):
        super(TestConv1d, self).__init__()
        self.conv = nn.Conv1d(in_channels=1, out_channels=1, kernel_size=3, padding=1, bias=False)
        self.init_weights()

    def forward(self, x):
        return self.conv(x)
    
    def init_weights(self):

jinglescode

class TestConv1d(nn.Module):
    def __init__(self):
        super(TestConv1d, self).__init__()
        self.conv = nn.Conv1d(in_channels=1, out_channels=1, kernel_size=3, bias=False)
        self.init_weights()

    def forward(self, x):
        return self.conv(x)
    
    def init_weights(self):

jinglescode

class TestConv1d(nn.Module):
    def __init__(self):
        super(TestConv1d, self).__init__()
        self.conv = nn.Conv1d(in_channels=1, out_channels=1, kernel_size=2, bias=False)
        self.init_weights()

    def forward(self, x):
        return self.conv(x)
    
    def init_weights(self):

jinglescode

class TestConv1d(nn.Module):
    def __init__(self):
        super(TestConv1d, self).__init__()
        self.conv = nn.Conv1d(in_channels=1, out_channels=1, kernel_size=1, bias=False)
        self.init_weights()

    def forward(self, x):
        return self.conv(x)
    
    def init_weights(self):

jinglescode

import numpy as np
import matplotlib.pyplot as plt


def generate_signal(length_seconds, sampling_rate, frequencies_list, func="sin", add_noise=0, plot=True):
    r"""
    Generate a `length_seconds` seconds signal at `sampling_rate` sampling rate. See torchsignal (https://github.com/jinglescode/torchsignal) for more info.
    
    Args:
        length_seconds : int

jinglescode

jinglescode

import torch
from torch import nn


class MultitaskSSVEP(nn.Module):
    """
    Using multi-task learning to capture signals simultaneously from the fovea efficiently and the neighboring targets in the peripheral vision generate a visual response map. A calibration-free user-independent solution, desirable for clinical diagnostics. A stepping stone for an objective assessment of glaucoma patients’ visual field.
    Learn more about this model at https://jinglescode.github.io/ssvep-multi-task-learning/
    This model is a multi-label model. Although it produces multiple outputs, we also used this model to get our multi-class results in our paper.
    

jinglescode

import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import TransformerEncoder, TransformerEncoderLayer


class TransformerModel(nn.Module):

    def __init__(self, ntoken, ninp, nhead, nhid, nlayers, dropout=0.5):