def get_board_info(board_type='synthetic', connection='usb', usb_port=None):
""" Gets the BarinFlow ID for the respective OpenBCI board in use
Parameters:
board_type (str): Type of base OpenBCI board (options: synthetic, cyton, daisy)
connection (str): Connection method, either via USB dongle or the Wifi board
Returns:
board_id (int): Id value for the board in the BrainFlow API
"""
params = BrainFlowInputParams()
if usb_port is None:
usb_port = STD_PORT
if board_type == 'synthetic':
board_id = BoardIds.SYNTHETIC_BOARD.value
elif board_type == 'cyton':
if connection == 'usb':
board_id = BoardIds.CYTON_BOARD.value
params.serial_port = usb_port
elif connection == 'wifi':
board_id = BoardIds.CYTON_WIFI_BOARD.value
elif board_type == 'daisy':
if connection == 'usb':
board_id = BoardIds.CYTON_DAISY_BOARD.value
params.serial_port = usb_port
elif connection == 'wifi':
board_id = BoardIds.CYTON_DAISY_WIFI_BOARD.value
return board_id, params
def get_board_info(board_type,
usb_port=None,
ip_addr=None,
ip_port=None,
serial_num=None):
params = BrainFlowInputParams()
if board_type == 'synthetic':
board_id = BoardIds.SYNTHETIC_BOARD.value
elif board_type == 'ganglion':
board_id = BoardIds.GANGLION_BOARD.value
params.serial_port = get_openbci_usb(usb_port)
elif board_type == 'cyton':
board_id = BoardIds.CYTON_BOARD.value
params.serial_port = get_openbci_usb(usb_port)
elif board_type == 'cyton_daisy':
board_id = BoardIds.CYTON_DAISY_BOARD.value
params.serial_port = get_openbci_usb(usb_port)
elif board_type == 'ganglion_wifi':
board_id = BoardIds.GANGLION_WIFI_BOARD.value
params.ip_address, params.ip_port = get_openbci_ip(ip_addr, ip_port)
elif board_type == 'cyton_wifi':
board_id = BoardIds.CYTON_WIFI_BOARD.value
params.ip_address, params.ip_port = get_openbci_ip(ip_addr, ip_port)
elif board_type == 'cyton_daisy_wifi':
board_id = BoardIds.CYTON_DAISY_WIFI_BOARD.value
params.ip_address, params.ip_port = get_openbci_ip(ip_addr, ip_port)
elif board_type == 'brainbit':
board_id = BoardIds.BRAINBIT_BOARD.value
if serial_num:
params.other_info = serial_num
elif board_type == 'unicorn':
board_id = BoardIds.UNICORN_BOARD.value
if serial_num:
params.other_info = serial_num
return board_id, params
#############################################################
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--serial-port', type=str, help='serial port',
required=False, default='/dev/ttyUSB0')
# if you are on Linux remember to give permission to access the port:
# sudo chmod 666 /dev/ttyUSB0
# or change the user group
# check BrainFlow documentation for Windows configs
args = parser.parse_args()
params = BrainFlowInputParams()
params.serial_port = args.serial_port
board = BoardShim(BoardIds.CYTON_BOARD.value, params)
board.prepare_session()
shared_vars = Shared()
mutex = threading.Lock()
board.start_stream() # use this for default options
acquisition = threading.Thread(target=acquire_signals)
acquisition.start()
computing = threading.Thread(target=compute_signals)
computing.start()
acquisition.join()
def run(self): # 线程执行函数
params = BrainFlowInputParams()
board_id = self.board_set[0]
self.c_value = []
params.serial_port = self.board_set[1]
self.board = BoardShim(board_id, params)
# 在线数据
#一导联数据和量导联数据
self.board.prepare_session()
self.board.start_stream()
time.sleep(3)
n = 0
m = np.zeros(2)
while self.is_on:
time.sleep(1)
data = self.board.get_current_board_data(self.board_set[3])
data = data[1:9]
mean_data = np.tile(
data.mean(axis=1).reshape(8, 1), (1, self.board_set[3]))
# print(data.shape)
data = data - mean_data
if self.puanduan1:
self.org_signal.emit(data)
filter1_data = self.processing_step1.step_1_list[
self.pre_set[0][0]](data, self.pre_set[0])
filter2_data = self.processing_step2.step_2_list[
self.pre_set[1][0]](filter1_data, self.pre_set[1])
if self.puanduan2:
# print(self.pre_set)
self.pre_signal.emit(filter2_data)
feature_data = self.feature.feature_list[self.class_set[0]](
filter2_data, self.class_set)
# print(np.argmax(feature_data))
if self.puanduan3:
self.feature_signal.emit(feature_data)
# print(feature_data)
result = np.argmax(feature_data)
r = feature_data[result]
# 更改部分（将程序中2s读取一次改为1s读取一次，每次512采样点改为256采样点）
if result == 5:
r = r + 0.1
elif result == 6:
r = r + 0.05
if r > 0.5:
n = n + 1
p = result + 1
print("分类结果：", p, " n = ", n)
m = np.append(m, p)
m = m[1:]
if m[0] == m[1]:
m[1] = 0
else:
b = str(p)
self.command_signal.emit(b)
print("发送指令：", p)